# Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A
Comprehensive Review

Meng Cui1, Xubo Liu1, Haohe Liu1, Jinzheng Zhao1, Daoliang Li3, Wenwu Wang1 M.
Cui, X.Liu, H. Liu, J. Zhao, and W. Wang are with the Centre for Vision,
Speech and Signal Processing (CVSSP), University of Surrey, Guildford GU2 7XH,
UK. (e-mail: [m.cui, xubo.liu, haohe.liu, j.zhao, w.wang]@surrey.ac.uk).D. Li
are with the National Innovation Center for Digital Fishery, China
Agricultural University, China (e-mail: dliangl@cau.edu.cn).

###### Abstract

Digital aquaculture leverages advanced technologies and data-driven methods,
providing substantial benefits over traditional aquaculture practices. Fish
tracking, counting, and behaviour analysis are crucial components of digital
aquaculture, which are essential for optimizing production efficiency,
enhancing fish welfare, and improving resource management. Previous reviews
have focused on single modalities, limiting their ability to address the
diverse challenges encountered in these tasks comprehensively. This review
provides a comprehensive analysis of the current state of aquaculture digital
technologies, including vision-based, acoustic-based, and biosensor-based
methods. We examine the advantages, limitations, and applications of these
methods, highlighting recent advancements and identifying critical research
gaps. The scarcity of comprehensive fish datasets and the lack of unified
evaluation standards, which make it difficult to compare the performance of
different technologies, are identified as major obstacles hindering progress
in this field. To overcome current limitations and improve the accuracy,
robustness, and efficiency of fish monitoring systems, we explore the
potential of emerging technologies such as multimodal data fusion and deep
learning. Additionally, we contribute to the field by providing a summary of
existing datasets available for fish tracking, counting, and behaviour
analysis. Future research directions are outlined, emphasizing the need for
comprehensive datasets and evaluation standards to facilitate meaningful
comparisons between technologies and promote their practical implementation in
real-world aquaculture settings.

###### Index Terms:

Digital aquaculture, fish tracking, counting, behaviour analysis

##  I Introduction

With the expansion of the global population and the degradation of the
ecological environment, traditional fishing (i.e. capture fisheries) is no
longer capable of meeting the growing human demand for fish products [1, 2].
Aquaculture has become the primary source of fish acquisition, and digital
aquaculture is emerging as a promising approach to enhance the efficiency and
sustainability of the industry [3].

Fish tracking, counting, and behaviour analysis are crucial components of
digital aquaculture, playing a vital role in effective management and
decision-making. Accurate monitoring of these aspects can help detect abnormal
fish behaviour, estimate fish abundance, and formulate reasonable management
strategies, ultimately improving fish welfare and economic outcomes in the
aquaculture industry [4]. Traditional methods for fish tracking and behaviour
analysis rely on the experience of human observers, and the observation
results depend on their skills and knowledge, which are not always reliable
[5, 6]. Similarly, manual fish counting methods involve removing fish from
tanks, leading to stress, injury, and disease, negatively impacting fish
welfare and growth [7, 8]. The implementation of intelligent tracking,
counting, and behaviour analysis technologies can help overcome these
limitations, reducing the risk of fish mortality, improving feeding
strategies, and promoting sustainable development in aquaculture [9, 10, 11].

Currently, various technologies such as vision-based sensors, acoustic-based
sensors and biosensors methods are used for fish tracking, counting, and
behaviour analysis in aquaculture. Vision-based sensors and computer vision
technology have found widespread application due to advancements in optical
imaging and computer vision. However, they are limited by poor illumination,
low contrast, high noise, fish deformation, frequent occlusion, and dynamic
backgrounds [12, 13, 14, 15]. Acoustic-based sensors and hydroacoustic
methods, which are non-invasive, are particularly useful for monitoring fish
in turbid water environments and overnight, but their high hardware cost
limits their popularity in intensive aquaculture settings [16, 17, 18, 19,
20]. Biosensors can provide valuable information on fish physiology and
behaviour, but their invasive nature and the need for individual fish tagging
can be challenging in large-scale aquaculture operations [21].

Previous reviews have been conducted on fish tracking, counting and behaviour
analysis [22, 23, 10, 24, 12, 25]. However, most reviews focus on computer
vision technology as the primary approach and relying on a single modality may
not provide sufficient data for comprehensive analysis. To address this
limitation, our paper systematically surveys vision-based sensors, acoustic-
based sensors, biosensors, and hydroacoustic methods, facilitating a holistic
discussion of tracking, counting, and behaviour analysis while identifying
technology gaps in the current literature.

This article comprehensively reviews the literature on fish tracking,
counting, and behavioural analysis in aquaculture over the past two decades,
emphasizing the progress made in these areas and identifying potential future
research directions. The remainder of this article is structured as follows:
Section II explores the advancements in fish tracking techniques, while
Section III discusses the various methods and applications of fish counting.
Section IV discuss the behaviour analysis of fish, and Section V presents an
overview of relevant public datasets. In Section VI, we examine the challenges
faced by the aquaculture industry and discuss future development trends.
Finally, Section VII summarises the key findings and conclusions presented in
this paper.

##  II Fish tracking

Vision-based multi-target tracking methods are increasingly used in fish
behaviour analysis. However, fish tracking is challenging because of the small
differences between individuals, complex environments, and variations in
plankton, shapes, angles, and scales of swimming fish [26]. Fish tracking can
be categorized into two-dimensional (2D) and three-dimensional (3D) tracking
based on the swimming environment [27]. 2D tracking is used in shallow water
containers, where fish swimming appears close to a 2D planar motion and is
represented using (x, y) coordinates, but can only analyze a part of the fish
behaviour. In contrast, 3D tracking considers depth information and is
represented using (x, y, z) coordinates, enabling the analysis of spatial
movement in natural environments.

In addition to vision-based tracking, acoustic techniques such as the Acoustic
Tag System (ATS) are also used for fish tracking. ATS involves attaching
acoustic tags to fish, which emit unique acoustic signals that are detected by
hydrophone receivers. The position of the tagged fish can be estimated using
the time difference of arrival of the acoustic signals at multiple receivers,
allowing for 3D tracking of fish movement in natural habitats [28] This
section mainly analyzes the relevant literature on fish tracking methods based
on visual technology (as shown in Table I) and acoustic techniques in recent
years and provides a systematic summary.

TABLE I: Summary of different methods in fish tracking. | Study
---
Site
| Maxmum Fish
---
Amounts
Points |  | Detection
---
Methods
| Tracking
---
Methods
| Tracking
---
Metrics
Advantages | Limitation | References
| Tank
---
13 |  | Fish’s
---
head
YOLOv2 |  | Kalman
---
Filter
| CIR
---
CTR
| High frame rate
---
not necessary
| Larger fish
---
quantities
increase
identification
losses
[29]
Tank | 11 |  | Fish
---
center
points
YOLOV3 |  | Euclidean
---
Distance
- |  | Enhance target
---
detection in
unclear water
| Fish numbers are
---
too small and
done in the
laboratory
[30]
Coast | - |  | Center of
---
the fish
head
YOLOv4 |  | Kalman
---
Filter
MOTA |  | Real-time
---
tracking with
high accuracy
| Accuracy affected
---
by different sea
areas
[31]
| Tank
---
5 |  | Fish head
---
and center
of the fish
body
| Background
---
subtraction
| Kalman
---
Filter
| CIR
---
CTR
| Accurate, fast,
---
and
computational
inexpensive
| Fail to predict the
---
motion state of
rapidly
transitioning
[32]
Tank | 20 |  | Fish head
---
and fish
body
| Background
---
subtraction
| Kalman
---
Filter
| CIR
---
CTR
| Smoother
---
resulting
trajectory
| Lower frame
---
speeds lead to
more track breaks
and higher
misidentification
[33]
Tank | 5 | Centroid |  | Background
---
subtraction
| Kalman
---
Filter
| CIR
---
CTR
| Enhances
---
tracking
performance
under occlusion
conditions
| Abnormal water
---
quality leads to an
increased chance
of fish body
overlap
[34]
Tank | - | Centroids | Otsu |  | Manhattan
---
Distance
- |  | Low cost and
---
removable
installation
| Stationary fish
---
mistaken for
debris or dead
[14]
| Tank
---
25 | Fish head | DOH | CNN | Recall |  | Corrects
---
trajectory
errors, fills
gaps, and
evaluates
credibility
| Easy affected by
---
floating objects,
ripple reflections,
fish sharp turns
[35]
Tank | 10 |  | Head
---
feature
point and
central
feature
point
| Background
---
subtraction
| Feature point
---
matching
| Precision
---
Recall
| Two-feature
---
point model
reduces
tracking
difficulty
| Only traces a few
---
objects for a very
short process
[27]
| Glass
---
Aquarium
5 |  | The head
---
and tail of
fish
| Adaptive
---
thresholding
algorithm
GNN |  | Tracking
---
errors
| Accurate
---
tracking by
pose constraint,
even at high
speed
| Unable to handle
---
fish occlusion or
attaching
[36]
| Fringing
---
Reef,
Red Sea
4 |  | Fish’s
---
body
| Fast-RCNN,
---
Inceptiont
V2
| Linking
---
consecutive
frames
| 3D
---
detection
rate
| Cost-effective,
---
automated 2D
track
Reconstruction
| Small groups of
---
fish studied
[37]
Tank | $50$ | Head | ResNet-101 |  | Mahalanobis
---
distance and
cosine
similarity
| MOTA
---
IDF1
| Performance
---
well under
multiple
negative factors
| Bad performance
---
of long-term
tracking
[38]
| Tank
---
Pond
$50$ | Body | Transformer |  | Hungarian
---
algorithm
| MOTA
---
IDF1
| Accommodates
---
individuals with
significant
appearance
variations.
| Limitations in
---
accurate ID
matching at high
stocking densities
(over 50 fish)
[39]
Tank | 8 | Head | LSTM |  | Kalman
---
Filter
| Precision
---
Recall
| Cross-view
---
more robust in
high densities
| Multi -view map
---
matching is
difficult, and the
calculation
amount is large
[40]
Tank | 49 | Head |  | Hessian
---
(DoH)
CNN
| Iterative
---
tracking
strategy
| Precision
---
Recall
| Tracking
---
individuals
exhibiting
frequent
occlusions
| Requires
---
individuals to
have at least one
body part that
remains robust
[41]

###  II-A Fish tracking based on 2-dimensional visual information

Fish tracking methods can be broadly categorized into three main approaches:
classical algorithms, kernel correlation Filter algorithms, and deep learning-
based tracking algorithms [25]. Each category encompasses various techniques
with their strengths and limitations, which will be explored in more detail in
the following subsections.

####  II-A1 Fish tracking based on classical algorithm

Classical algorithms have been widely used to address the challenges of fish
tracking in complex underwater environments, such as rapid posture changes,
occlusion, overlap, and poor image quality. The Tracking-Learning-Detection
(TLD) algorithm, which updates salient features and target model parameters
through online learning, has shown promise in providing stable tracking [42].
However, its median-flow tracker may fail when fish change their swimming
posture rapidly. An adaptive scale mean-shift (ASMS) algorithm, utilizing fish
shape and colour features, has replaced the median-flow tracker and handled
posture changes, uneven illumination, and complex backgrounds [43].

Preserving individual fish identities during occlusion and overlap remains a
significant challenge. Techniques that extract head shape or body geometry
features have been explored [44, 45], but their effectiveness may be limited
by the rapid movement and intense geometry of fish bodies [14]. Adaptive
threshold algorithms, which estimate thresholds for each pixel based on its
adjacent region, have shown promise in segmenting individual fish in binarized
images [46]. The global nearest neighbour algorithm with fish posture as a
tracking constraint has been used to track small numbers of zebrafish [36],
but it lacks individual recognition ability, leading to track exchanges during
overlap or occlusion. The Toxld algorithm addresses this issue using intensity
histograms and Hu-moments to link trajectory fragments and preserve individual
fish identities [47]. However, the error increases with the number of fish.

To mitigate poor image quality, retinex (MSR) based enhancement algorithms
combined with object detection and Euclidean tracking have been used to
improve fish detection in unclear underwater images [30, 48, 49]. Kalman
Filters may not always be optimal for underwater fish tracking due to non-
Gaussian noise and complex environments [50]. Mean offset technology, which
models fish probability density based on colour histograms, can fail when the
background colour closely resembles the fish colour distribution. Tracking
algorithms based on covariance representation, which model objects as
covariance matrices of pixel-based feature sets, incorporate spatial and
statistical characteristics, making them more suitable for tracking fish in
challenging underwater scenarios [51, 52].

####  II-A2 Fish tracking based on Kalman Filter

The Kalman Filter, an efficient autoregressive filter that estimates the state
of a dynamic system in an environment with uncertainties, has been widely used
for fish tracking due to its versatility and robustness [53, 33]. Building
upon the Kalman Filter, the SORT (Simple Online and Realtime Tracking)
algorithm has emerged as a simple yet effective multi-target tracking approach
[34]. SORT utilizes a Kalman Filter for frame-by-frame data correlation, and
the Hungarian algorithm for correlation measurement [54]. Despite its good
performance at high frame rates, SORT has limitations, such as ignoring object
surface features, which makes the tracking results heavily dependent on the
detection performance [55]. To address this issue, an extension of SORT called
DeepSORT was developed, which leverages a CNN model trained on large-scale
pedestrian datasets to extract features that enhance the network’s robustness
to loss and obstacle [56].

Recent literature shows that DeepSORT, an extension of the SORT algorithm, has
been extensively applied in fish tracking [29]. DeepSORT combines the Kalman
Filter-based SORT framework with a deep learning-based appearance feature
extractor, enabling more robust tracking performance. However, challenges
arise when fish undergo rapid body shape changes during fast turns, leading to
blurry and difficult-to-track images [32]. To mitigate this issue, shorter
exposure times and variable-size boundary boxes can be used, with the boundary
boxes being estimated according to the motion state. Despite these challenges,
the Kalman Filter remains a popular choice for fish tracking due to its
ability to estimate the state of a dynamic system in the presence of
uncertainties.

The frame rate plays a crucial role in Kalman Filter-based fish tracking
performance. Low frame rates can lead to increased tracks and a higher
likelihood of misidentification. Conversely, high frame rates result in more
linear fish motion, enabling Kalman Filters to predict individual motion more
accurately (as shown in Fig. 1). As the field continues to advance, future
research should prioritize optimizing tracking schemes to minimize computing
time and evaluating the long-term tracking performance of these methods in
diverse aquatic environments.

Figure 1: Fish trajectory under different frame rates.

####  II-A3 Fish tracking based on deep learning

Deep learning has emerged as a powerful tool for fish tracking, with Tracking
by Detection (TBD) being the primary approach. In TBD, a deep learning model
is trained on a large dataset to learn convolutional features with strong
expressive power, enabling the detection and tracking of fish in video
sequences. However, occlusion between fish remains a significant challenge in
TBD methods, often leading to the generation of fragmented trajectories that
require post-processing to link them together [57].

To address the occlusion issue, several notable multi-target tracking
algorithms have been proposed, such as idTracker [58] and its upgraded
version, idtracker.ai [59]. These algorithms extract unique fingerprint
features from each animal in a set of videos and then identify each target in
the video, enabling the tracking of individuals within a group by
automatically identifying untagged animals. Although these methods have been
widely used for tracking juvenile fish and small animals, the experimental
setup restricted fish from swimming up and down to avoid overlapping,
simplifying the task compared to real-world 3D tracking scenarios.

Further advancements in deep learning-based fish tracking have been achieved
by combining CNN-based methods with other techniques, such as head detection,
motion state prediction, and verification using SVM classifiers [41]. These
approaches have demonstrated more robust tracking performance compared to
idTracker when the fish density is higher, and the occlusion frequency
increases, highlighting the potential of deep learning in handling complex
tracking scenarios [35].

Despite the progress made in controlled laboratory environments, real-world
marine environments pose additional challenges for fish tracking, such as
light fluctuations and waves. To address these issues, To tackle these issues,
researchers have developed methods like the real-time multi-class fish stock
statistics method (RMCF), which uses YOLOv4 as the backbone network and adopts
a parallel two-branch structure based on deep learning for detecting fish
species, tracking, and counting fish [31]. Although these methods have shown
promising results in complex marine environments, their recognition accuracy
may vary in different sea areas due to differences in colour cast and
contrast, necessitating the retraining of the network weight coefficients.

Siamese network trackers have gained attention in recent years due to their
exceptional tracking speed and high accuracy. The introduction of advanced
algorithms, such as SiamRPN++ (as shown in Fig. 2), has further highlighted
the performance of Siamese networks, surpassing the performance of tracking
algorithms based on correlation filters [60], [61]. Although there are
currently few articles on Siamese networks specifically for fish tracking,
this approach is expected to become a new direction in the field.

Figure 2: Fish tracking method based on YoloV5 and SiamRPN++ [61].

Moreover, the emergence of transformer-based tracking methods has
revolutionized the field of object tracking. Initially proposed for natural
language processing tasks, transformers have been successfully adapted for
computer vision tasks, including object detection and tracking [62].
Transformer-based trackers, such as TransTrack [63] and STARK [64], have
demonstrated state-of-the-art performance on various tracking benchmarks.
Transformer-based tracking methods have also shown promising results in fish
tracking applications [39]. As transformer-based methods continue to advance
in object tracking, they are expected to play an increasingly important role
in fish-tracking applications. Future research should focus on further
adapting transformer architectures to the specific challenges of underwater
environments and developing efficient training strategies to handle the
limited availability of annotated fish-tracking datasets.

###  II-B Fish tracking based on 3-dimensional visual information

3-D tracking methods offer advantages over 2-D tracking algorithms, as they
can be used to study the behaviour of social animals and effectively address
most occlusion problems. However, 3-D tracking also presents significant
challenges due to the large number of fish, similar individual appearance,
occlusion, and uncertainty of stereo matching.

Figure 3: Three methods to measure the 3-D position of a fish in an aquarium.

Two main types of 3-D tracking methods have been developed: ”shadow” and
”stereo” methods (as shown in Fig. 3). The ”shadow” method, which requires
only one camera, uses the shadow of the fish projected onto the substrate as a
second view of the shoal. By calculating the 2-D positions of the fish and its
shadow, the 3-D position of the fish can be obtained through triangulation.
However, this method becomes increasingly difficult as the number of fish
increases and shadows may be obscured, as it requires detecting each fish and
its corresponding shadow.

Stereoscopic methods use multiple cameras to capture simultaneous images at
different angles or a camera and a mirror [22]. Some researchers have
developed platforms that use a single camera and mirror to obtain 3-D
coordinates and automatically track fish [65, 66]. These methods calculate the
centre coordinates of fish and combine the association of mirror view and
direct view for tracking, addressing the problem of target loss caused by
occlusion. However, they require high-precision equipment and may suffer from
correspondence deviations due to the pixel centres of real and virtual fish
not being at the same point. Moreover, these methods are not suitable for
actual production environments.

In theory, two cameras are sufficient for stereo imaging. 3-D tracking with
two cameras involves obtaining the 2-D motion trajectory from the top view
(larger viewing angle) and then performing 3-D matching of the top view
tracking results with the feature points in the side view to obtain the
object’s movement in 3-D space (as shown in Fig. 4) [67]. Three or more high-
speed cameras are usually required to capture synchronous videos to track many
objects, solve ambiguities, and avoid errors between objects. A study by [68]
determined the location of fisheye under the top and side views using mixed
Gaussian and Gabor models, respectively, and then obtained the 3-D motion
trajectories of the objects by associating the top-view tracking results with
the trajectories of two side views [40]. However, the detection effect has
poor performance due to the difficulty in distinguishing the eye area
characteristics of fish. Furthermore, analyzing fish movement behaviour in
three views leads to complex equipment installation and reduces the accuracy
of association and stereo matching [67].

Figure 4: Three-dimensional trajectory of multiple fish in water tank via
multi video tracking.

Occlusion remains one of the main challenges in 3D fish tracking, as it is in
other MOT (Multiple Object Tracking) tasks. However, the frequency of
occlusion has not been adequately measured in the current literature, with the
complexity indicator of the datasets used in existing studies typically being
the number of fish rather than an assessment of fish occlusion events. For
instance, a demo video in [27] shows only 4 occlusion events within 15 seconds
for a group of 10 fish.

Current system evaluations assess parameters such as ID swaps, fragments,
precision, and recall for the generated 2-D and 3D tracks without describing
how these indicators are calculated. The lack of uniform indicators makes it
difficult to fairly compare the methods presented in various studies.
Furthermore, most of the literature does not provide open-source code and
annotated data, limiting the repeatability of the results. A recent study by
[69, 38] introduced a standard MOT evaluation framework for fish tracking,
providing a good model for multi-target fish tracking. A unified evaluation
standard should be introduced to ensure the fairness of fish multi-target
tracking comparisons and facilitate progress in this field.

###  II-C Fish tracking based on acoustic tag system

The Acoustic Tag System (ATS), a passive acoustic method of acoustic
monitoring technology, has become an important means of monitoring fish
trajectories and studying fish behaviour [70]. Unlike vision-based tracking
methods, which rely on clear water conditions and sufficient lighting, ATS can
provide reliable tracking data in challenging underwater environments, such as
turbid waters or low-light conditions [71]. The appropriate acoustic tag (also
called an acoustic signal transmitter) type and parameters are selected
according to the size of the fish and the research period (as shown in Fig. 5)
[72]. The application of the acoustic tagging system mainly includes the
abundance assessment of fish resources, the swimming pattern of fish, the
evaluation of habitat characteristics, the spawning site of fish, the survival
situation of fish, and the behaviour differences of fish [73, 74]. However,
acoustic tag monitoring technology is rarely used in aquaculture and has broad
application prospects.

Using acoustic tag monitoring technology to monitor fish movement and
behaviour trajectories, obtain real-time three-dimensional movement trajectory
coordinates of the fish, and perform related data analysis and application is
an advanced technique [75]. Compared with vision-based monitoring
technologies, acoustic tag monitoring technology has the advantages of in-situ
observation and simple data processing methods. However, this technology
determines the location of the fish by receiving the sound wave signal sent by
the acoustic tag on the fish, and the fish may die during the data monitoring
process [76, 77]. Therefore, technicians must conduct real-time monitoring and
data processing and analysis of monitoring data promptly to ensure the
continuity and accuracy of the data.

Acoustic tag monitoring technology and vision-based tracking methods have
their unique strengths and limitations. Vision-based methods can provide
detailed information about fish appearance, shape, and motion, but they are
limited by water clarity and lighting conditions. In contrast, acoustic tag
monitoring technology can provide reliable tracking data in challenging
underwater environments, but it lacks detailed visual information about fish
appearance and behaviour. Combining these two modalities can help overcome
their limitations and provide a more comprehensive understanding of fish
behaviour and movement patterns.

Figure 5: Fish tracking method based on acoustic tag[78].

###  II-D Tracking evaluation metrics

Multi-target tracking evaluation indices directly reflect an algorithm’s
tracking ability, and the MOTchallenge official multi-objective tracking
evaluation indicators [79] provide a standardized framework for assessment.
Key metrics include Multiple Object Tracking Accuracy (MOTA) and Multiple
Object Tracking Precision (MOTP).

The MOTA combines three sources of errors to evaluate a tracker’s performance,
follow as:

$MOTA=1-\frac{\Sigma_{t}\left(FN_{t}+FP_{t}+ID_{SW_{t}}\right)}{\Sigma_{t}GT_{t}}$
(1)

where $FN_{t},FP_{t},ID_{sW_{t}},GT_{t}$ represent the number of false
negatives, false positives, identity switches, and ground truth targets in
frame $t$ correspondingly.

The MOTP is used to measure misalignment between annotated and predicted
object locations, defined as:

$MOTP=\frac{\sum_{i,t}d_{t}^{i}}{\sum_{t}c_{t}}.$ (2)

where $d_{t}$ is the distance between the localization of objects in the
ground truth and the detection output $c_{t}$ is the total matches made
between ground truth and the detection output.

Identification-Score ($IDF_{1}$) comprehensively considers Identification
Precision ($IDP$) and Identification Recall ($IDR$) rate:

$IDF_{1}=\frac{TP}{TP+0.5FP+0.5FN}$ (3)

where True Positive ($TP$), False Positive $(FP)$, and False Negative $(FN)$
involved in $IDF_{1}$ all consider ID, so the indicator is more sensitive to
the accuracy of ID information.

To better capture the specific challenges of tracking fish populations, some
literature has introduced additional metrics, such as Correct Tracking Ratio
(CTR) and Correct Identification Ratio (CIR). CTR measures the percentage of
correctly tracked frames for individual fish.

$CTR=\frac{\sum(\text{ NumberOfCorrectFramesOfSingleFish })}{\text{
NumberOfFish }\times\text{ NumberOfFrames }}$ (4)

$CIR$ represents the probability of correctly identifying all fish after an
occlusion event:

$CIR=\frac{\text{ TimesThatAllFishGetCorrectIdentityAfterOcclusion }}{\text{
NumberOfOcclusionsEvents }}$ (5)

In addition to those metrics, tracking speed is another important factor to
consider when evaluating fish-tracking algorithms, especially for real-time
applications. Some common metrics for measuring tracking speed include frames
per second (FPS) and processing time per frame. FPS indicates the number of
frames a tracking algorithm can process in one second while processing time
per frame measures the average time taken to process a single frame. Higher
FPS and lower processing time per frame are desirable for efficient and real-
time tracking performance. These metrics offer a valuable foundation for
evaluating fish tracking performance, comprehensively assessing various
errors, fish-specific challenges, and tracking speed. However, they may not
always capture the full complexity of fish-tracking scenarios and can be
limited by the lack of widespread adoption and the need for detailed ground-
truth annotations.

To drive advances in this field, researchers should work towards developing
more specialized metrics and evaluation protocols that consider the specific
requirements and challenges of fish tracking applications. By combining these
metrics with careful consideration of the diverse underwater environments in
which fish tracking algorithms must operate, researchers can work towards more
comprehensive and standardized evaluation practices that fully characterize
the robustness, generalizability, and efficiency of these algorithms.

##  III Fish counting

###  III-A Fish counting methods based on sensor technology

Sensor-based counting devices are usually divided into resistance counters and
infrared counters. Infrared counters detect infrared signals, which are
electromagnetic waves with wavelengths between 760 nm and 1 mm [80]. Counting
based on infrared counters requires a tunnel structure to limit the movement
of the fish. When a fish passes between the infrared transmitter and the
receiver, the counting is completed [81, 82]. Although infrared sensors can
count in smaller areas of space, their performance is affected by water depth
and turbidity. At a depth of 17.9 centimetres (cm) in pure water, the
intensity of the infrared light drops to 50% [83], and the presence of
suspended particles can further degrade the performance of infrared counting
at high turbidity levels [84]. In addition to environmental factors, the
accuracy of infrared sensor counting devices is susceptible to the pass rate
of fish, often resulting in an underestimation of the number of fish [85, 86].
This may be due to the slow swimming of some fish, confusion when two or more
fish enter the scanner unit simultaneously, and the reluctance of some fish to
leave the device after entering the light tunnel, resulting in repeated
scanning. Despite these limitations, infrared light can work in the dark, and
the accuracy of counting can be improved by subsequent software algorithms,
such as multiple object tracking (MOT) algorithms, which can solve false
counting from multiple targets [84, 87, 88].

Resistivity counters, another type of sensor-based counting method, work by
detecting changes in resistance when a fish passes between two electrodes [89,
90]. Like infrared sensor counting devices, electronic resistivity counters
require the fish to pass through a specific tunnel and have similar
disadvantages, such as repeat counts when a fish swims multiple times in the
channel and missing counts when the number of fish is large [91]. However,
electronic resistivity counters are suitable for limited lighting and long,
narrow river channels while detecting non-destructively and without requiring
specific lighting conditions [92].

Although both infrared and resistivity fish counters have limitations and may
underestimate fish pass rates, they offer valuable tools for non-invasive fish
counting in various environments. Future research should focus on developing
and improving these technologies to enhance their accuracy and reliability.
Potential avenues for improvement include modifying resistivity counters and
exploring alternative sensors [93]. By addressing the current challenges and
refining these sensor-based counting methods, researchers can provide valuable
tools for effective fishery management and conservation efforts.

###  III-B Counting methods based on computer vision technology

Accurate fish biomass assessment is crucial for optimizing management
strategies and reducing feeding costs in the aquaculture industry [94, 95].
Computer vision-based fish counting has gained prominence among various
methods due to its non-invasive nature, low cost, and high efficiency [96,
97]. However, the complexity of underwater environments, including varying
light conditions, backgrounds, and fish swimming patterns, poses challenges
for accurate fish counting [98, 99, 100].

This section summarizes and analyzes the current computer vision-based fish
counting aquaculture methods, focusing on two main categories: image-based
counting and video-based counting. The summary of the computer vision-based
methods can be seen in Table. II. The subsections delve into the details of
each category, discussing the advancements, challenges, and future directions
in the field. By bridging the gap between laboratory-based experiments and
real-world applications, computer vision-based fish counting can become an
indispensable tool for sustainable aquaculture management.

####  III-B1 Image-based counting method

Image-based fish counting methods can be broadly categorized into two main
approaches: detecting-based methods, which aim to detect all fish in a region,
and density-based methods [101, 102], which estimate the number of fish by
analyzing the distribution of fish schools [103, 104].

TABLE II: Different counting methods based on computer vision. | Study
---
Cite
Amount | Dataset | Model | Count points |  | Evaluation
---
index
Results | Advantages | Limitations | References
Tank | 100 | 786 | $\mathrm{MAN}$ | Center | Accuracy | $97.12\%$ |  | Better
---
generalization
ability
| larger error for
---
areas with high
fish density
[105]
Tank | - | 4000 | DG-LR |  | Fish-
---
Connected
Area
$\mathrm{R}^{2}$ | $96.07\%$ |  | No need to detect
---
every fish
| No complex
---
environments
[7]
| Net
---
Cage
214 | 1501 |  | Hybrid Neural
---
Network
Center points | Accuracy | $95.06\%$ |  | Improves model
---
performance
without losing
resolution
| Does not
---
describe the
distribution of
fish school
gathering and
dispersing
[106]
Cage | 62 | 200 | $\mathrm{RCNN}$ | Bounding Box | Accuracy | $92.4\%$ |  | Reduce count
---
errors due to
repeating
detections
| Repeating
---
detection and
wrong detection
in high contrast
areas
[107]
Counter | 1000 | 1500 |  | Background
---
Subtraction
Kalman filter
Blob |  | average
---
precision
$97.47\%$ |  | Automatic
---
counting, low
cost
| No detailed
---
analysis of the
number of fish
in the system
per unit time
[108]
Containers | 600 | 4000 | $\mathrm{CNN}$ | Contours | Accuracy | $99.17\%$ |  | Threshold adapts
---
to different
numbers of fish
| Pure white
---
background, no
noise
[109]
Dishpan | 100 | - |  | Local
---
Normalization
Filter
Pixel Area |  | Accuracy
---
F-measure
| $99.8\%$
---
$98.83\%$
| automated
---
system.
| Small sample
---
size
[110]
Aquarium | 350 | 1000 |  | Background
---
Subtraction
Contours | Accuracy | $95.57\%$ |  | Portable, low
---
cost
| Need a fixed
---
size of fish and
a certain area
[111]
Aquarium | 9 | - |  | Adaptive
---
Thresholding
Skeleton |  | Average
---
counting error
$6\%$ |  | Solve the
---
overlapped-fish
problem cleverly
| Only adapted to
---
relatively small
fish densities
[112]
Net Cage | 250 | 1000 | $\mathrm{PTV}$ | Centroid | Detection rate | $90\%$ |  | Potential
---
application or
industrial
aquaculture
| Affected by
---
background
noise sensitivity
[113]
Aquarium | 100 | 600 | LS-SVM | Skeleton | Accuracy | $98.73\%$ |  | Good
---
generalization
| Assume that the
---
size of fish is
similar
[114]
Aquarium | 300 | 3200 | MSENet | Centroid | MAE | 3.33 |  | Lightweight and
---
low
computation
costs
| limited to a
---
scene with a
fixed
viewpoint
[115]
Long Channel | 300 | 1318 | YOLOv5-Nano | Bounding Box |  | Average
---
Counting
Precision
$96.4\%$ |  | Solves the
---
problem of
missing fish fry
| Occlusion still
---
causes some
fish to be
incorrectly
detected
[116]

Early studies focused on detecting-based methods, which rely heavily on the
accuracy of fish image segmentation from the background [117]. These methods,
such as artificial neural networks (BPNN) [118], showed potential for
automatic fish counting in scenarios with a limited number of fish. However,
they often struggled with complex adhesions in fish images and overlapping
fish [119, 111]. To address the challenges of overlapping fish, adaptive
segmentation algorithms were developed to extract the geometric features of
fish [114]. Combined with machine learning models like LS-SVM, these
algorithms showed improved counting accuracy compared to BPNN models,
particularly in scenarios with similar fish sizes and low stocking densities.
However, the performance of these models declined when faced with high fish
densities and changing geometric shapes due to fish overlap [120]. Further
advancements in fish image segmentation were made by introducing more general
adaptive thresholding methods and skeleton extraction-based methods to handle
overlapping fish [112]. While these methods performed well under controlled
laboratory conditions, their accuracy diminished in real-world aquaculture
environments, where factors such as high fish school density, poor visibility,
and insufficient light posed significant challenges [113].

Efforts to mitigate issues related to light, noise, and feature recognition
led to the development of segmentation methods that combined local normalized
filters and iterative selection thresholds [110]. Although these methods
demonstrated high performance in correcting non-uniform lighting, reducing
noise, and identifying features, the unique challenges posed by aquaculture
settings, such as fish shadows caused by water refraction and continuous
movement of shoals, continued to affect segmentation accuracy and limit the
effectiveness of traditional computer vision methods for fish counting [111].

The introduction of deep learning techniques has opened new avenues for fish
counting in aquaculture. With the increasing availability of fish datasets,
deep learning models have been applied to this domain, offering strong
adaptability and easy transformation without requiring complex feature
extraction work [121, 107]. Convolutional neural networks (CNNs) have been
shown to achieve high accuracy in detecting and counting fish of different
sizes by adjusting different thresholds [109].

Density-based methods, which estimate the number of fish by mapping input
images to corresponding density maps, have also shown promise in fish counting
applications. These methods provide additional information about the spatial
distribution of fish, which can be valuable for various purposes [122]. Hybrid
neural network models, such as those combining MCNN and DCNN architectures,
have been proposed to improve fish counting accuracy, outperforming
traditional CNNs and MCNNs [97, 105].

Despite the advancements made in fish counting methods, several challenges
remain. Density-based methods are sensitive to the degree of occlusion, with
higher fish densities leading to greater errors. Moreover, variations in water
quality, light conditions, camera angle, water depth, and surface refraction
can cause significant differences in the appearance of fish across different
farming environments, affecting the accuracy and generalization ability of
counting models. To address these challenges, future research should create
more comprehensive and diverse datasets that capture the variability
encountered in real-world aquaculture settings. Efforts should also be
directed towards improving counting accuracy, model generalization ability in
high-density areas, and maintaining accuracy under different pond conditions.

####  III-B2 Video-based counting method

Video-based counting methods offer a more efficient alternative to counting
objects in single images, enabling the development of reliable and inexpensive
systems for counting fish in sequential videos [123]. However, directly
applying current automatic detecting and counting frameworks in underwater
environments presents several challenges. Firstly, underwater cameras are
susceptible to contamination by impurities in the water, leading to
deterioration of video quality. Additionally, current communication technology
and cost limit underwater video real-time transmission technology, resulting
in delays that impact real-time detection [124]. Secondly, underwater videos
suffer from colour shift and contrast degradation due to light absorption and
scattering in the water, making object detection and segmentation more
difficult than in land-based applications [125].

To address these issues, numerous image enhancement algorithms have been
proposed to improve the quality of underwater images [126]. These algorithms
aim to restore the real colour and improve the contrast of underwater images.
However, the effectiveness of these enhancement techniques varies greatly
depending on the environment and lighting conditions [127]. Furthermore, the
detection and segmentation results directly depend on the image enhancement
and segmentation performance, which can be time-consuming.

Despite these challenges, video-based counting methods find applications in
various aquatic environments, such as aquaculture and fisheries management. In
aquaculture, these methods can be used to estimate fish catch and abundance
statistics, reducing the time and effort required for manual recording by
fishermen [128, 129]. In the context of stream fish research and management,
video technology provides a new strategy for estimating fish abundance,
although its effectiveness may vary depending on the age and behaviour of the
fish species being studied [130, 131].

As technology advances and more robust algorithms are developed, video-based
counting methods are expected to play an increasingly important role in
accurately assessing fish populations in various aquatic environments.
Combining video methods with machine learning models promotes powerful new
directions for river fish research, management, and protection. Future
research should focus on improving the reliability and efficiency of these
methods while addressing the specific challenges posed by underwater video
acquisition and analysis, such as image quality degradation, real-time
transmission limitations, and the need for effective image enhancement
techniques. By tackling these issues, video-based counting methods can provide
valuable insights into fish populations and support sustainable fisheries
management practices.

###  III-C Counting methods based on acoustic technology

Acoustic technology for fish counting can be divided into two main categories:
acoustic imaging and hydroacoustic methods. While underwater visible imaging
suffers from limitations due to light attenuation caused by water absorption
and scattering, resulting in blurred images and reduced image quantity as
shooting distance increases, acoustic-based counting methods offer a viable
alternative. Sound waves can travel far through water without significant
attenuation, making them suitable for situations where visual counting is
inappropriate or ineffective.

####  III-C1 Acoustic imaging methods

Multi-beam imaging sonar such as Adaptive Resolution Imaging Sonar (ARIS) and
Dual-frequency Identification Sonar (DIDSON) are normally used to monitor
migratory fish in rivers [132]. These systems produce high-resolution
underwater sonar video output without the need for underwater light, allowing
for fish counting and measuring directly from the footage, even in turbid
waters and overnight [133].

DIDSON, developed by the Applied Physics Laboratory at the University of
Washington [134], is a multi-beam sonar system frequently used to acquire
underwater acoustic images for fish identification and counting. As DIDSON
uses sound instead of light, it is not affected by water turbidity and can
collect data during both day and night [135, 136]. However, studies have shown
that manual counting of DIDSON data can be time-consuming and prone to errors,
with large deviations between operators [137, 138]. This may be because
Echoview repeatedly calculated nearly stationary horizontal positions within
the DIDSON field of view [139].

Figure 6: Example of DIDSON image counting [140].

To reduce the time and cost of DIDSON data processing, various subsampling
methods can be employed, with automation-assisted subsampling being the best
method to reduce the cost of estimating migratory fish populations in rivers
[18]. Multi-beam echogram processing software, such as Echoview or DIDSON
Control and Display software, can partially perform fish detection and
counting functions [141, 142]. Echoview uses a Component Object Model (COM)
interface that allows users to build customized pre-processing and post-
processing scripting modules, streamlining the processing method and providing
the ability to refine fish counting using various fish detection parameters
[143, 144]. However, the echograms of the video-like data files generated by
DIDSON require manual counting, which is tedious, time-consuming, and can
produce large errors for large datasets [145]. Semi-automatic post-processing
of imaging sonar data is possible using existing software (e.g., Echoview
Software Pty Ltd., Hobart, Australia) [141, 146]. but the process still
requires manual calibration for non-fish target noise, which is cumbersome and
inefficient. Furthermore, post-processing software can be very expensive,
limiting its accessibility for many researchers and practitioners.

Digital image-processing technology offers an inexpensive and rapid
alternative that has been successfully applied in various scientific fields.
Several studies have focused on the automatic processing of fish targets in
imaging sonar data. For example, K-nearest neighbour background subtraction
with DeepSort target tracking to track and count fish automatically [147] and
GPNet, a novel encoder-decoder network with global attention and point
supervision, to boost sonar image-based fish counting accuracy [148].

The new generation of acoustic cameras includes Adaptive Resolution Imaging
Sonar (ARIS) (Sound Metrics Corp, WA, USA), which operates at higher
frequencies compared to DIDSON, offering greater flexibility and improved
image resolution [149, 150]. A comparison of fish monitoring data based on the
ARIS sonar system and the GoPro camera showed that the detection rate of the
sonar-based system was 62.6% (compared to the amount captured by the net),
exceeding the 45.4% of the camera-based system [151].

While sonar imaging counting methods are powerful tools for gathering fish
abundance estimates in difficult-to-observe, structurally complex, chaotic,
and dark environments, they can still be disturbed by various types of
underwater noise. Additionally, sonar imaging equipment is relatively
expensive and requires professional personnel to conduct analysis, making it
more suitable for investigating fish abundance in ocean fishing and river
ports [152, 153].

These recent advancements in digital image-processing techniques showcase the
growing interest in developing efficient and accurate methods for automatic
fish tracking and counting in sonar data. By leveraging the power of deep
learning and computer vision algorithms, these approaches aim to overcome the
limitations of manual processing and provide more reliable and scalable
solutions for aquaculture monitoring and management. However, while these
methods show promising results, they still face challenges such as dealing
with occlusions, varying fish densities, and the need for large annotated
datasets for training. Future research should address these limitations and
develop more robust and generalizable algorithms that can be easily adapted to
different sonar imaging systems and underwater environments.

Table III summarizes the advantages and disadvantages of sonar imaging
counting methods in aquaculture and their practical applications. Despite the
limitations, acoustic counting methods remain valuable for monitoring fish
populations in challenging underwater environments where visual counting
methods may be impractical or ineffective.

TABLE III: A comparison of different methods based on acoustic imaging. Site | Technology | Software | $\mathrm{MHz}$ | Metrics | Results | Advantages | Limitations | References
---|---|---|---|---|---|---|---|---
River | ARIS | Echoview |  | $1.1\mathrm{MHz}$
---
frequency
Accuracy | $84\%$ |  | Distinguishes
---
downstream
moving fish from
other objects
| Results vary
---
among operators
[16]
Lagoon | ARIS | Sound Metrics | $1.8\mathrm{MHz}$ | $R^{2}$ | 0.99 |  | Consistent results
---
with manual
| The results varied
---
greatly among
different
operators
[137]
River | ARIS |  | ARIS
---
software Fish
$1.8\mathrm{MHz}$ | F1-scores | $75\%$ |  | Faster and no
---
post-processing
| Underestimates
---
total fish count
[154]
Reservoir | ARIS |  | KNN
---
background
subtraction and
DeepSort
$1.8\mathrm{MHz}$ | Accuracy | $73\%$ |  | Automatic
---
calibration saves
data processing
time
| Unable to
---
identify fish in
bottom
background, long
processing time
[147]
River | ARIS | ARISfish | $3.0\mathrm{MHz}$ | Detection Rate | $62.6\%$ |  | Counts fish $>100$
---
mm in night and
turbid conditions
| May not detect
---
small fish
[151]
River | DIDSON | Echoview 6.0 | $1.8\mathrm{MHz}$ | Accuracy | $83.7\%$ |  | Avoids manual
---
counting errors
and biases
| Time-consuming
---
calculations
[18]
River | DIDSON | Sound Metrics | $1.8\mathrm{MHz}$ | F1 scores | $79\%$ |  | Performs well
---
using direct,
shadow, and
combined
detections
| Low fish
---
densities in each
image
[140]
Reservoir | DIDSON |  | NN-EKF2/
---
Echoview
$1.8\mathrm{MHz}$ |  | Error compared
---
with the manual
detection results
Less than $5\%$ |  | Less calculation
---
and easy to
implement
| Inaccurate when
---
targets overlap
[153]
River | DIDSON |  | Sound Metrics/
---
Echoview
$1.2\mathrm{MHz}$ | Accuracy |  |  $90\%$ (upstream)
---
$41\%$ (downstream)
| Estimates
---
potamodromous
fish passage in
large lakes
| High processing
---
times and costs
[139]
River | DIDSON |  | Manual
---
counting
$1.8\mathrm{MHz}$ |  | Average Percent
---
Error (APE)
$5.4\%$ |  | Not limited by
---
surface
disturbances or
turbidity
| Shadowing from
---
passing fish
| [138]
---
River | DIDSON | Hand-counter | $1.8\mathrm{MHz}$ |  | Coefficient of
---
Variation (CV)
$9.63\%$ |  | Better acoustic
---
target
identification and
resolution
| Data loss on
---
small fish in
highly turbulent
environments
[152]

####  III-C2 Hydroacoustic methods

Acoustic echo-sounding is one of the most popular methods for estimating fish
abundance due to their simplicity and non-invasive nature [155]. These methods
rely on the physical characteristics of the target and the water medium. When
an echo sounder’s transducer emits an acoustic wave, it spreads through the
water and encounters the target object. Due to the difference in acoustic
impedance between the object and the water medium, the object scatters the
incident acoustic wave, and a portion of it is backscattered to the
transducer, known as the echo signal [156, 157].

The target’s depth can be measured according to the interval between the
acoustic emission and the reception of the target’s echo. By analyzing the
strength and structure of the echo signal, the intensity, number, and
distribution of the target can be estimated. The Echo Integration method is
one of the main methods for underwater acoustic assessment of fish stocks. It
calculates the number of fish by dividing the integral value of the echo
intensity of fish in the sampling unit area by the ultrasonic reflectance of
individual fish (target intensity, TS). Several studies have used the echo
integration technique to estimate the number of fish based on the
backscattering echoes observed with an echo sounder [158, 159].

Although the sound intensity reflected by a shoal is related to the number of
fish [160], the use of echo sounders in fish tanks and cages presents several
challenges [161]. Reverberation in a cage can occur due to the echo of an
acoustic signal from the boundary, necessitating the removal of the cage
boundary signal during counting [162]. Another issue with acoustic estimation
of fish populations is shadow utility, which is needed to compensate for the
attenuation of echo strength when dense shoals are in focus [157]. To
investigate the possibility of using commercial echo sounders for real-time
fish counting in offshore cages, a study by [163] employed an echosounder and
echo-integration technology. The experimental results showed that the proposed
method could achieve more than 90% estimation accuracy [164], indicating its
reliability for future fish management decisions.

Despite the increasing use of underwater echo sounders in fishery research,
their application is subject to interference from various factors, such as
differences in instrument performance, the blind area of the echo sounder
itself, external environmental factors, and the evasive behaviour of fish in
response to survey ships and sound waves [165, 166]. Furthermore, echosounders
are expensive and technically demanding, making them unsuitable for factory
aquaculture needs. Future research should focus on reducing the cost of
instruments or developing alternative instruments suitable for promotion to
meet the actual needs of aquaculture.

##  IV Fish school behaviour analysis

Fish behaviour, a direct result of the living environment and growth state,
includes both normal (i.e. feeding behaviour, swimming behaviour, reproduction
behaviour, gathering behaviour) and abnormal behaviours (i.e. disease
behaviour, hypoxia behaviour, cannibalism behaviour) [167, 168, 169]. Poor
water quality and management in aquaculture can cause fish stress behaviour
[170], leading to immune suppression, slow growth, and reduced productivity
and welfare [171]. Traditional fish behaviour analysis, relying on human
observers, is often unreliable, time-consuming, and labour-intensive [5, 6].
Accurate estimation of fish behaviour is crucial for optimizing resource use,
controlling water quality, and improving fish welfare and economic benefits
[172]. The following sections will explore the latest advancements in fish
behaviour analysis, focusing on computer vision-based methods for assessing
fish school behaviour and feeding behaviour, providing insights into the
current state of the art and potential future directions for research and
application in this field.

Figure 7: Abnormal behaviors: “Turning-over behavior”, “Frightening behavior”,
“Feeding behavior”, “Hypothermia behavior”, “Hypoxic behavior”, “Cannibalism
behavior”.

###  IV-A Fish school behaviour analysis based on computer vision

####  IV-A1 Fish feeding behavior

In intensive aquaculture, feeding is the main expenditure [173], and feeding
optimization is crucial for improving efficiency and reducing costs [174].
Traditional feeding methods based on farmers’ experience are limited by low
efficiency and high labour intensity, and they cannot accurately address the
problems of overfeeding or underfeeding [175]. The intensity and amplitude of
changes in fish behaviour can directly reflect fish appetite. Computer vision
technology can effectively quantify fish feeding behaviour, optimize feeding
strategies, and reduce feeding costs.

Many researchers have used traditional methods, such as background subtraction
and optical flow, to extract target features for determining feeding indices
[176]. While these methods can accurately capture fish feeding behaviour, they
require complex foreground segmentation processes that may decrease
computational efficiency and are easily affected by water surface fluctuations
and reflective areas [20]. With its advantages of automatic feature extraction
and large-capacity modelling, deep learning has been widely used in
aquaculture [177].

Existing approaches mainly use digital cameras to capture the corresponding
images as input and characterize the fish behaviour with discrete feeding
intensity (e.g., “None”, “Weak”, “Medium” and “Strong” [178, 179, 180, 181])
as a classification problem, modelled by Convolutional Neural Networks (CNNs).
However, fish-feeding behaviour is a dynamic and continuous process. Single
images are insufficient to capture the context of fish feeding intensity [177,
182]. As an alternative, video-based methods have been proposed to exploit
spatial and temporal visual information for FFIA, which offers rich context
for capturing fish feeding behaviour. Raw RGB videos were converted into
optical flow image sequences and fed into a 3D convolutional neural network
(3D CNN) to evaluate fish feeding intensity, achieving a very high accuracy
[183, 184].

While recent advancements in computer vision and deep learning have shown
promise in analyzing fish feeding behaviour, some limitations still need to be
addressed. One major challenge is the discrepancy between the ideal
environments in which fish-feeding datasets are collected and the real-world
conditions found in aquaculture settings. Factors such as water turbidity,
fluctuating light levels, and variable camera angles can significantly impact
the performance of these models when deployed in real-world farms.

Another limitation is the computational complexity of video-based models,
which often require substantial computational resources, making them difficult
to deploy on resource-constrained devices commonly used in aquaculture. The
large size of these models can also hinder their real-time performance, which
is crucial for timely decision-making in aquaculture management. Furthermore,
the limited generalizability of current models to new fish species is a
significant challenge. Many existing models are trained on species-specific
datasets, and their performance often drops significantly when applied to new
or unseen species due to differences in morphological features, colour
patterns, and behavioural characteristics.

To address these limitations, future research should focus on developing more
robust, adaptable, and species-agnostic models that can effectively handle the
variability encountered in real aquaculture environments. This may involve
collecting more diverse and representative datasets, exploring domain
adaptation, transfer learning, and few-shot learning techniques, and
optimizing models for efficient inference on edge devices.

####  IV-A2 Hypoxia behavior

Hypoxia, a common issue in aquaculture systems, can significantly impact fish
mortality and lead to substantial production losses [185]. Fish exhibit
various behavioural responses to hypoxic conditions, such as changes in
ventilatory frequency (VF), swimming activity, surface respiration, and
vertical habitat [186, 187, 188, 189]. To provide early warning of hypoxia in
aquaculture, it is essential to evaluate the specific behavioural responses of
fish when oxygen levels in the water drop sharply.

Image processing algorithms have been proposed to quantify the hypoxia
behaviour of fish in aquariums [190]. However, these methods often rely on
complex foreground segmentation processes, which can decrease computational
efficiency and are easily affected by water surface fluctuations. Deep
learning methods, such as YOLO object detection, have emerged as powerful
tools for transforming and upgrading fish farming practices by quickly
detecting fish behaviour with high accuracy [173].

Despite the progress made in recognizing fish hypoxia behaviour, most
experiments have been conducted under laboratory conditions, which may not
accurately reflect the challenges encountered in actual production systems.
Factors such as water turbidity, uneven illumination, and high fish density
can make it more difficult to identify individual fish and their specific
behaviours in real-world settings. Furthermore, inducing hypoxia through human
intervention in laboratory experiments can compromise animal welfare and cause
irreversible damage to fish health.

To address these limitations, future research should focus on developing more
robust and adaptable methods for detecting fish hypoxia behaviour in real-
world aquaculture systems. Moreover, integrating multiple data sources, such
as water quality sensors and video monitoring systems, could provide a more
comprehensive understanding of fish behaviour and enable early detection of
hypoxia-related issues. By combining advanced computer vision techniques with
domain expertise in aquaculture and fish physiology, researchers can develop
more effective and practical solutions for monitoring and managing fish health
in real-world settings.

####  IV-A3 Other abnormal behavior

Abnormal fish behaviours, such as aggression, fear, stress, illness, parasitic
infection, and cannibalism, can have significant impacts on aquaculture
production (as shown in Fig. 7), fish welfare, and population balance [169,
191, 192, 193]. While less common than feeding and hypoxia behaviours, these
abnormalities still play a crucial role in aquaculture warning operations.
Detecting and localizing abnormal behaviours, particularly those occurring
within small groups or individuals, remains challenging in computer vision. To
address this challenge, researchers have adapted techniques from human
behaviour analysis, such as motion-effect maps and deep learning algorithms,
to detect, localize, and recognize abnormal fish behaviours in intensive
aquaculture systems [194, 195]. These methods have shown promising results in
identifying specific behaviours and evaluating various health and
environmental factors. However, further research is needed to investigate the
complex interplay between local and global abnormal behaviours and develop
robust, multi-target tracking systems that operate efficiently in real-world
aquaculture settings.

Monitoring and protecting fish during critical life events, such as spawning
aggregations, is essential for maintaining population balance and preventing
overfishing [196, 197]. Computer vision techniques, including stereoscopic
video analysis and 3D neural networks, have been employed to quantify fish
reproductive behaviour and classify complex behaviours [198, 199], providing
valuable tools for baseline studies and long-term monitoring.

While computer vision and image processing technologies offer economical and
effective means for monitoring abnormal fish behaviour, the relative scarcity
of abnormal behaviour data has hindered in-depth research. Most existing
studies have been conducted in controlled laboratory environments, which may
not accurately represent the complex factors in real-world aquaculture
settings [200, 201]. Overcoming the challenges posed by complex water
environments, uneven lighting, large numbers of individuals, and intricate
fish movements is crucial for developing robust and reliable multi-target
abnormal behaviour monitoring and tracking systems in computer vision [61].

###  IV-B Fish school behaviour analysis based on Trajectory analysis

Visual-based monitoring systems for detecting abnormal fish behaviour often
rely on known scenes and predefined movement models, which can be subjective
and lack adaptability to different environments [202]. Analyzing many target
trajectories in a specific scene can reveal behaviour patterns and construct
effective motion behaviour models with greater universality and applicability
[182].

Using visual monitoring systems, researchers can obtain 3D time-varying
trajectory data (location and time information) of fish. Studies have tracked
zebrafish using YOLOV2 and Kalman filters, obtaining movement trajectories
that showed significantly faster swimming, greater agitation, and
agglomeration in the centre of the aquarium during feeding periods [29]. Other
researchers have developed semi-automatic in situ tracking systems to
reconstruct synchronized 3D movement trajectories of individual reef fish in
social groups, analyzing their behaviour when capturing plankton prey [37].

However, relatively few studies on abnormal fish behaviour use trajectory
analysis in aquaculture. This scarcity can be attributed to the limited
availability of open datasets on abnormal fish behaviour and the rare
occurrence of such behaviours, making data acquisition challenging. Moreover,
fish trajectories inherently contain information about position, speed, and
direction, and the definition of an abnormal trajectory may encompass multiple
aspects. In the past decade, track-based anomaly detection methods have
primarily relied on traditional clustering methods or focused on statistical
models of trajectories. In contrast, the representation of trajectories
remains an open problem [203, 204].

To address these challenges and advance aquaculture, future research should
draw inspiration from the successful application of abnormal trajectory
behaviour analysis methods in other fields, such as crowd and vehicle
monitoring. By adapting these techniques to fish’s unique characteristics and
environments, researchers can create more powerful and flexible models for
identifying and comprehending abnormal fish behaviour in various aquaculture
contexts.

###  IV-C Fish behaviour analysis based on passive acoustic monitoring

Passive acoustic monitoring (PAM) has emerged as a non-invasive and
increasingly accessible remote sensing technology for monitoring underwater
environments [205, 206]. With approximately 1,000 out of the 35,000 known fish
species confirmed to produce sounds underwater [207, 208], PAM offers a unique
opportunity to analyze fish behaviour through the sounds they generate (The
example of fish abnormal behaviour is shown in Fig. 8).

Fish can produce a series of sounds during feeding, and the frequency spectrum
of these sounds can be used to analyze their feeding behaviour. For example,
turbots generate feeding sounds that vary with food intake intensity, ranging
from $15$ to $20$ dB in the frequency range $7$–$10$ kHz [209]. Similarly,
feeding sounds produced by various fish species, such as rainbow trout
($0.02$–$25$ kHz) [210], Japanese minnow ($1$–$10$ kHz) [211], Atlantic horse
mackerel ($1.6$–$4$ kHz)[212], yellowtail ($4$–$6$ kHz) [213], have comparable
frequency ranges.

The Fish feeding behaviour analysis based on audio was initially proposed by
[4, 214], the audio signal is first transformed into log mel spectrograms and
then fed into a CNN-based model for FFIA. Subsequent work [215, 216] have
further demonstrated the feasibility of using audio as input for FFIA. Audio-
based methods offer advantages such as energy efficiency and lower
computational costs compared to vision-based methods [217, 218]. However,
audio-based models have lower classification performance than video-based FFIA
due to their inability to capture full visual information and sensitivity to
environmental noise [219]. Moreover, rapidly swimming predatory fish, such as
brown and rainbow trout, often combine forward swimming with feeding,
accompanied by splashing sounds and strong tail patting [172]. The rapid
pellet capture by these species superimposes feeding sounds, and pellet
impacts pose a challenge in obtaining accurate feeding sound data.

To overcome these challenges, future research should focus on developing
advanced signal processing techniques to separate feeding sounds from ambient
noise and other interfering sounds. Additionally, exploring the integration of
audio and visual data could help improve the overall classification
performance and robustness of fish behaviour analysis systems.

Figure 8: The audio spectrum of different fish abnormal behaviours [220].

###  IV-D Fish behaviour analysis based on biosensor technology

Biosensor technology has shown great potential in collecting individual animal
information, such as individual trajectory, acceleration, velocity,
respiration frequencies, heartbeat frequency, and tail beat frequency [221,
222]. In recent years, accelerometers have been increasingly used in marine
biology research to study the feeding behaviour of aquatic animals.

The feeding behaviour of most fish leads to characteristic changes in
acceleration that differ from their normal movement patterns [223]. These
characteristic changes in acceleration can be effectively used to distinguish
feeding behaviour patterns from other behaviour patterns [224]. For example,
[225] used accelerometer tags to investigate the feeding behaviour of Atlantic
cod (Gadus morhua) in the wild. The authors found that the accelerometer data
could accurately identify feeding events and provide insights into the
foraging ecology of this species. Similarly, a study by [226] used a
combination of accelerometers and gyroscopes to analyze the feeding behaviour
of captive yellowtail kingfish (Seriola lalandi). The authors demonstrated
that the sensor data could be used to classify different types of feeding
behaviour, such as biting, chewing, and swallowing, with high accuracy.

In addition to feeding behaviour, biosensors have been used to study other
aspects of fish behaviour, such as swimming activity and energy expenditure.
For instance, [227] used accelerometers to investigate the swimming behaviour
and energy expenditure of wild Atlantic salmon (Salmo salar) while migrating
to spawning grounds. The authors found that the accelerometer data provided
valuable insights into the swimming performance and energy costs of this
species in natural conditions.

However, using biosensors in fish behaviour analysis also presents some
challenges and concerns. Biosensors are typically surgically attached or
implanted into the fish’s body, which can lead to the direct death of the fish
or cause behavioural changes that may affect the results of experiments.
Moreover, this method may cause irreversible harm to the fish and compromise
animal welfare. To address these issues, researchers should focus on
developing minimally invasive or non-invasive biosensor technologies that can
be safely attached to or removed from fish without causing undue stress or
harm. Furthermore, ethical considerations should be prioritized when using
biosensor technology in fish behaviour analysis.

Despite these challenges, biosensor technology offers a promising approach to
studying fish behaviour at the individual level, providing valuable insights
into the feeding ecology, swimming performance, and energy expenditure of
various fish species. By combining biosensor data with other monitoring
techniques, such as passive acoustic monitoring and vision-based methods,
researchers can develop a more comprehensive understanding of fish behaviour
in both captive and wild settings. As biosensor technology continues to
advance, it is essential to balance the potential benefits of these tools with
the need to ensure the welfare and ethical treatment of the fish being
studied.

##  V Public dataset

High-quality public datasets are crucial for developing and evaluating
computer vision and deep learning methods for fish detection, tracking, and
behaviour analysis. However, despite the growing popularity of deep learning,
there are still relatively few public datasets specifically focused on
underwater fish scenes. This scarcity has led many researchers to conduct
their analyses and behavioural studies under ideal or controlled conditions.
Table IV summarizes the available public fish datasets.

TABLE IV:  Summary of the various fish datasets. Dataset |  | No. of
---
videos/image
Resolution |  | Number of
---
labeled fish
Tasks | Reference
| Fish4-
---
Knowledge
| 700,000 videos
---
with $10\mathrm{~{}min}$
each clip
$320\times 240$ | - |  | Classification,
---
Detection and
Tracking
[228]
SeaCLEF 2016 |  | Training set: 20
---
videos and
20,000 images,
Test set: 73
videos
| $640\times 480$
---
$320\times 240$
9000 |  | Classification,
---
Counting
[229]
NCFM |  | 16,915 images
---
$(3,777$ training,
13,138 testing)
$1920\times 1080$ | 10000 |  | Detection,
---
classification and
counting
| [230]
---
| Sonar image
---
counting dataset
| 30 videos
---
sequence with
537 images
$360\times 360$ | - | Counting | [231]
3D-ZeF20 |  | Training Set:
---
54052 images,
Test Set: 32400
images
$2704\times 1520$ | 86452 | Tracking | [69]
| Automated
---
Fish Tracking
| 189 videos of
---
varying durations
(1- 30 seconds)
$1920\times 1080$ | 8700 |  | Detection,
---
Tracking
[232]
DeepFish | 39,766 images | $1920\times 1080$ | 3200 |  | Segmentation,
---
counting and
Classification
[233]
FISHTRAC | 14 videos | $1920\times 1080$ | 3449 |  | Tracking and
---
detection
| [234]
---
BrackishMOT |  | 98 videos each
---
lasting about 1
minute
$2704\times 1520$ |  | Tracking | [235]
$\mathrm{CFC}$ |  | 527215
---
SONA images
|  $288\times 624$ to
---
$1086\times 2125$
515933 |  | Detection, Tracking
---
and Counting
[236]
| Mullet Schools
---
Dataset
| over 100k
---
SONA images
| $320\times 576$
---
500 |  | Detection
---
Counting
[237]
| Fish Sounds
---
| 115 different fish
---
sound clips
| 64kbps
---
- |  | Behaviour
---
analysis
[238]
| AV-FFIA
---
| 27000 video
---
and sound clips
| $1086\times 2125$
---
256kbps
All |  | Feeding Behaviour
---
analysis
[214]

##  VI Challenges and future perspectives

Fish tracking, counting, and behaviour analysis play a crucial role in the
intelligent development of aquaculture production. While computer vision
technology is currently a popular method for these tasks, it faces several
challenges due to the unique characteristics of aquaculture environments, such
as high fish density, complex water backgrounds, and irregular fish movement.
These factors can lead to interference between multiple targets, false
detections, missed counts, and tracking failures.

Acoustic methods offer an alternative approach that enables automatic and
rapid fish counting and tracking in low-light and turbid water conditions.
However, underwater noises, high equipment costs, and the need for
professional expertise make acoustic methods more suitable for large-scale
operations like marine fishing rather than factory or pond farming
environments. To further increase the level of intelligence in aquaculture, we
predict several different trends for future development:

1) Massively available datasets: The wide application of intelligent
technology in aquaculture, especially the success of deep learning algorithms
in image processing [15], has highlighted the need for large-labelled
datasets. Although available datasets are gradually increasing, most are
limited to identifying and detecting fish species. Open data on fish tracking,
counting, and behaviour analysis is scarce. Passive acoustic monitoring (PAM)
is also gaining popularity for underwater listening [239, 206] and public
sound data of underwater fish (e.g., Fishsound) are emerging. However, the
sample size of these datasets has not yet reached critical mass. In the
future, developing an international platform for sharing images and acoustic
data will be essential to promote sustainable aquaculture development.

2) Audio-visual multi-modal techniques: Fish tracking, counting, and behaviour
analysis methods are limited to single modalities (acoustic or computer
vision). However, the complex aquaculture environment leads to one-sided data
that cannot fully capture all fish information [214]. Multimodal machine
learning aims to establish models that process and associate data from
multiple modalities. With the development of multimodal machine learning,
crowd tracking and behaviour analysis based on audio-visual data have
attracted extensive attention. Multimodal learning for fish is still in its
infancy, but combining video data, sonar imaging data, and active acoustic
data can better model fish tracking, counting, and behaviour quantification
tasks, further improving the level of intelligence in aquaculture.

3) On-device machine learning: Most current fish tracking, counting, and
behavioural analysis models are performed in the cloud or on high-performance
GPUs. However, many aquaculture tasks require real-time responses, such as
fish feeding and abnormal behaviour detection. Cloud-based models may struggle
to guarantee this real-time performance, and many devices in remote and harsh
aquaculture environments may not have consistent internet connectivity. On-
device models can greatly reduce exercise pressure and make devices more
intelligent, providing users with a better experience. However, terminal
devices have processing power, power consumption, cost, and volume
limitations. Future developments could focus on reducing the complexity of
computing and storage by optimizing neural network algorithms or compressing
network models using techniques like knowledge distillation to enable direct
running on device chips.

4)Integration of fish tracking, counting, and behaviour analysis: Most
research addresses fish tracking, counting, and behaviour analysis as separate
tasks. However, these tasks are often interconnected in real-world aquaculture
scenarios and must be performed continuously in the same environment.
Developing a joint model that can handle all three tasks simultaneously would
be more memory-efficient and suitable for practical applications in
aquaculture. A joint model would leverage the shared features and information
among the tasks, reducing redundancy and improving overall performance. For
example, accurate fish tracking can provide valuable information for counting
and behaviour analysis, while behaviour analysis can help identify and resolve
tracking challenges such as occlusions and interactions between fish.
Researchers can develop more comprehensive and efficient systems for
monitoring and managing aquaculture farms by integrating these tasks into a
single framework. This approach would also reduce the computational resources
required, making it more feasible to deploy such systems in real-world
settings. Future research should focus on developing novel architectures and
training strategies that can effectively combine fish tracking, counting, and
behaviour analysis tasks.

5) Integration of large language models (LLMs) and artificial general
intelligence (AGI): Recent advancements in LLMs and AGI have the potential to
revolutionize fish tracking, counting, and behaviour analysis. LLMs, such as
GPT-4 [240] and LLaMA [241], can be fine-tuned on aquaculture-specific
datasets to generate accurate descriptions and analyses of fish behaviour from
textual data. AGI systems, like DeepMind’s Gato [242], which can perform a
wide range of tasks using a single model, could be adapted to integrate
multiple modalities (e.g., vision, acoustics, and text) for comprehensive fish
monitoring and management. By leveraging the power of LLMs and AGI,
aquaculture researchers and practitioners can develop more intelligent and
adaptable systems for understanding and optimizing fish welfare and
production.

##  VII Conclusion

This review provides a comprehensive analysis of the current state of digital
technologies in aquaculture, including vision-based sensors, acoustic-based
sensors, and biosensors, for fish tracking, counting, and behaviour analysis.
These technologies offer valuable tools for optimizing production efficiency,
fish welfare, and resource management in aquaculture. However, each technology
has its own limitations, such as the sensitivity of vision-based sensors to
environmental conditions, the high cost and complexity of acoustic-based
sensors, and the potential invasiveness of biosensors. Despite the
advancements made in these technologies, significant challenges remain,
including the scarcity of comprehensive fish datasets, the lack of unified
evaluation standards, and the need for more robust and adaptable systems that
can handle the complexities of real-world aquaculture environments. To address
these challenges and drive progress in the field, future research should focus
on developing diverse and representative datasets, establishing standardized
evaluation frameworks, and exploring the integration of multiple technologies
to create more comprehensive and reliable monitoring systems. Furthermore,
emerging technologies such as multimodal data fusion, deep learning, and edge
computing present exciting opportunities for advancing digital aquaculture. By
leveraging these technologies, researchers can develop more accurate,
efficient, and practical solutions for fish tracking, counting, and behaviour
analysis, ultimately contributing to the sustainable growth and development of
the aquaculture industry.

##  VIII AUTHOR CONTRIBUTIONS

Meng Cui: Conceptualization; writing – original draft. Xubo Liu:
Investigation; validation; methodology; data curation. Haohe Liu: Validation;
writing – original draft; data curation; formal analysis. Jinzheng Zhao:
Validation; writing – review and editing. Daoliang Li: Funding acquisition;
validation; project administration. Wenwu Wang: Funding acquisition;
validation; project administration; resources; writing-review and editing.

##  IX ACKNOWLEDGMENT

This work was supported by the Research and demonstration of digital cage
integrated monitoring system based on underwater robot [China grant
2022YFE0107100], Digital Fishery Cross-Innovative Talent Training Program of
the China Scholarship Council (DF-Project) and a Research Scholarship from the
China Scholarship Council (202006350248).

##  X DATA AVAILABILITY STATEMENT

Since this is a review paper, there is no data available. All information can
be found in the cited references.

##  XI CONFLICT OF INTEREST STATEMENT

The authors declare that there are no conflicts of interest.

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