We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away . In addition , we present preliminary results from activity recognition using low-resolution depth video data from seven apartments , and classify four activities - no movement , standing up , sitting down , and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response ( IIR ) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification . We develop algorithms to autonomously identify sensor contexts and propose a gating function to automatically activate the most accurate machine learning model among the set of shared expert models . We assess the performance of Share-n-Learn for activity recognition when a dynamic sensor constantly migrates from one body location to another . Our analysis based on real data collected with human subjects on three datasets demonstrates that Share-n-Learn achieves , on average , 68.4 % accuracy in detecting physical activities with context-varying wearables . Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift . In this work , we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives , including architecture , algorithm design and system implementation . First , the Edge computing architecture is introduced and several critical management tasks are also investigated . In this paper , we propose a framework , based on the notion of online learning , for real-time and on-device machine learning training . We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers . Our results , based on Pegasos online learning , demonstrate that the proposed approach achieves 97 % accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40 % . This is yet to be explored . In order to investigate this question , in this paper , we explore how these various motion sensors behave in different situations in the activity recognition process . For this purpose , we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions . The dataset comprises of RGB-D images , point cloud data , automatically generated skeleton tracks in addition to crowdsourced annotations . We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking . These researchers need a simulator that can synthesize realistic sensory datasets . To solve this problem , we propose the ' UbiSim ' simulator for activity recognition research . UbiSim provides a 3D graphical user interface to enable spatial perception using multiple sensors , including those that detect motion , pressure , vibration , temperature , and contact , along with RFID tags and receivers . A variety of experiments have been conducted in the field , which include realistic scenarios of different types of walking/digging . The results of experiments show that an accuracy of more than 90 % and a false alarm of around 5 % can be achieved in real time for activity detection and recognition . Thus , action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing . In order to remain true to the underlying biophysics of electrically excitable cells , we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes . Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley , Morris-Lecar , Fitzhugh-Nagumo , as well as simpler models of the integrate-and-fire and threshold-and-fire type . The model unit is an analytical model ( S. Kalitzin et al. , Epilepsy Behav . 22 ( 2011 ) S102-S109 ) and does not correspond directly to realistic neuronal processes ( excitatory-inhibitory synaptic interactions , action potential generation ) . For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models . One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the " building blocks of movement generation " , called motor primitives . Motor primitives , as used in this paper , are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties . Different from the traditional robot action generation methods , our system generates the robot action and its parameters only from the HMM which is learned from the recognition phase . In this paper , it is a very important point that the robot can recognize and generate action using an identical HMM , and the robot do not need to record any trajectory data for the action generation using transitional method , such as Dynamic Movement Primitives ( DMPs ) . Since the robot action and its parameters are generated from HMMs , we can adjust the parameters to change the robot action speed . The second aims at providing tools which facilitate video editing and enable to create interactive and structured video documents ( hyper-video ) . This last application includes the generation of actions when designating a pre-indexed area in the image . This is of particular interest for education , tourism , teleshopping . The original work from which these structures are drawn has been carried out by researchers in a range of fields including anthropology , sociology and social psychology : the ' local approach ' described in this paper draws particularly on conversation analysis . We briefly discuss the application of heuristics derived from this work to the development of an interaction tracking system and , in more detail , discuss the use of this work in the development of an architecture for generating action for an interactive agent . Describes the application of a domain-independent planner , COLLAGE , ( Co-Ordinated Localized aLgorithms for Action Generation and Execution ) , to the task of planning data-retrieval and data-preparation steps performed by Earth scientists . They describe the nature of this process , its inherent difficulties , the potential benefits of automation , and why AI-based planning is particularly suitable for carrying out this automation . Translation is not only translating words from one language into another , but it is a universal and more complex process . The research question is : how do elements of the communication and translation process in MACS affect the action generation process within organisations ? As a result , the study proposes a communication model of MACS . We first enhance a transition-based approach with additional actions that aim to handle particularities in the structure of the AMR . We analyze actions to address multi-aligned nodes and non-projective word orders , and explore several algorithms for action sequence generation , which incorporate the newly proposed actions . Secondly , we explore strategies for tackling AMR re-entrant concepts , which represent co-references in the associated textual data . A benchmark evaluation of the state-of-the-art approaches for action recognition is also provided on our dataset . Furthermore , we propose an approach by integrating the NTS-Net into two-stream network so as to locate the most informative regions and extract more discriminative features for fine-grained action recognition . Our experiments show that the proposed approach significantly outperforms the existing approaches . In order for action recognition to be useful in sports analytics a finer-grained action classification is needed . For this reason we focus on the fine-grained action recognition in tennis and explore the capabilities of deep neural networks for this task . In our model , videos are represented as sequences of features , extracted using the well-known Inception neural network , trained on an independent dataset . Hand gestures are normally classified into multi-modal actions , including static gestures , fine-grained dynamic gestures , and coarse-grained dynamic gestures . Among them , the fine-grained action detection is limited under the small-scale image region condition . To solve this problem , we propose the HandSense , a new system for the multi-modal HGR based on a combined RGB and depth cameras to improve the fine-grained action descriptors as well as preserve the ability to perform general action recognition . Secondly , the inferred hand/object posi- tion and interaction status are utilized to provide 1 ) more compact feature pooling by effectively pruning large num- ber of motion features from irrelevant spatio-temporal po- sitions and 2 ) discriminative action detection by a granu- larity fusion strategy . Comprehensive experiments on two challenging fine-grained activity datasets ( i.e. , cooking ac- tion ) show that the proposed framework achieves high ac- curacy/robustness in tracking multiple mutually occluded hands/objects during manipulation as well as significant performance improvement on fine-grained action detection over state-of-the-art methods . Recent research in the area of action recognition has focused on coarse-grained action recognition , and there have been few studies on fine-grained action recognition . In response to this phenomenon , we propose a method for fine-grained action recognition using a deep convolutional network . This method uses the I3D network , which has achieved great success in the area of coarse-grained action recognition , as the basic network architecture . At the same time , the human pose and hand are extracted for obtaining local features of the fine-grained action . We describe a DNN for fine-grained action classification and video captioning . It gives state-of-the-art performance on the challenging Something-Something dataset , with over 220 , 000 videos and 174 fine-grained actions . In this paper , we develop an effective method of detecting and tracking hands in uncontrolled videos based on multiple cues including hand shape , skin color , upper body position and flow information . We apply our hand detection results to perform fine-grained human action recognition . We demonstrate that motion features extracted from hand areas can help classify actions even when they look familiar and they are associated with visually similar objects . Abstract This paper addresses the problems of both general and also fine-grained human action recognition in video sequences . Compared with general human actions , fine-grained action information is more difficult to detect and occupies relatively small-scale image regions . While several computer aided FoG event detection methods have been proposed , they were not particularly designed for video data collected during clinical diagnosis . In this paper , we treat video based FoG detection as a fine grained human action recognition problem and reduce the interference of visual content which is irrelevant to FoG , such as gait motion of supporting staff involved in clinical assessment . In order to effectively characterize FoG patterns , we propose to identify anatomic patches as the candidate regions which could be relevant to FoG events , and formulate FoG detection as a weakly-supervised learning task . On the other hand , for some well-trained traditional supervised learning models , it is a waste of time to collect enough samples of new categories and retrain them together in order to identify new categories . We proposes a mapping between visual features of video and semantic description of fine-grained human action recognition . Unlike most current zero-shot learning methods , which use manual features as visual features , we uses features learnt from I3D network model as visual features , which are more general than manual features . When performed automatically by computers , it has many practical applications , such as in human - robot interaction , human behaviour analysis and image descriptions for the visually impaired . Furthermore , detailed part information has been shown to be beneficial in other visual recognition tasks such as fine - grained recognition [ reference ] , human pose estimation [ reference ] and object detection [ reference ] . section : Conclusion We have proposed a system for joint object detection and part localization capable of state - of - the - art fine - grained object recognition . Our method learns detectors and part models and enforces learned geometric constraints between parts and with the object frame . The integration of unlabeled data regularizes the CNN learning process . We show that the LSRO method is superior to the two available strategies for dealing with unlabeled data ; and a demonstration that the proposed semi - supervised pipeline has a consistent improvement over the ResNet baseline on three person re - ID datasets and one fine - grained recognition dataset . section : Related Work An action can be represented as a sequence of salient postures . Effective modeling of the salient postures is critical for robust action recognition . This paper proposes to characterize the salient postures using a set of the spatio-temporal interesting points ( STIPs ) . In this paper , we propose a novel temporal spiking recurrent neural network ( TSRNN ) to perform robust action recognition in videos . The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals . We use structure from motion with the physical scale and gravity direction as an initialization of our bundle adjustment for force estimation . Our method shows quantitatively equivalent reconstruction comparing to IMU measurements in terms of gravity and scale recovery and outperforms method based on 2D optical flow for an active action recognition task . We apply our method to first person videos of mountain biking , urban bike racing , skiing , speedflying with parachute , and wingsuit flying where inertial measurements are not accessible . Evaluation shows that the proposed features are discriminative and computationally efficient . Our method achieves state-of-the-art performance with the standard human action recognition benchmarks , namely KTH and Weizmann datasets . During classification , we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence . The proposed approach achieves state-of-the-art performance with standard human action recognition benchmarks , namely KTH and UCF-sports datasets and competitive results for the Hollywood ( HOHA ) dataset . But the high computational complexity of optical flow hinders these methods from applying to realtime scenarios . In this paper , we advance an efficient camera motion estimation and compensation method for realtime action recognition by exploiting motion vectors in video compressed-domain ( a.k.a . compressed-domain global motion estimation , CGME ) . Viewpoint variation is a major challenge in video- based human action recognition . We exploit the simultaneous RGB and Depth sensing of RGB-D cameras to address this problem . We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure : the addition problem , the multiplication problem and the MNIST classification problem using sequence of pixels . In addition , we present results for a benchmark action recognition problem . Finally , we learn a classification matrix by iterative optimization . Empirical studies on benchmark tasks of action recognition demonstrate that our algorithm can improve classification accuracy significantly . Motivated by the idea that domain knowledge is of great concern in sports field , we propose a keyframe based temporal segment network ( KTSN ) for classification and achieve remarkable performance . Experimental results demonstrate that FSD-10 is an ideal dataset for benchmarking action recognition algorithms , as it requires to accurately extract action motions rather than action poses . We hope FSD-10 , which is designed to have a large collection of finegrained actions , can serve as a new challenge to develop more robust and advanced action recognition models . In this paper , we propose a novel spatio-temporal feature which is useful for feature-fusion-based action recognition with Multiple Kernel Learning ( MKL ) . The proposed spatio-temporal feature is based on moving SURF interest points grouped by Delaunay triangulation and on their motion over time . The analysis is characterized by working on sub-sequences of the entire video signals , processing a small fragment of the whole image , estimating the location of the region of interest , using simple operations ( sum , subtraction , multiplications , divisions ) , extracting a reduced number of features per frame ( 6 features ) , and using a combination of four linear classifiers ( one perceptron and three support vector machines ) with a hierarchical structure . The method is evaluated on two of the datasets cited in the human action recognition literature , the Weizmann and the UIUC datasets . Results show that for the case of the Weizmann dataset , the correct classification rate ( CCR ) is 99.95 % when the LOOCV Protocol is used and 98.38 % for the case of Protocol 60 - 40 , which is comparable or even higher than that of current state-of-the-art methods .
We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away . In addition , we present preliminary results from activity recognition using low-resolution depth video data from seven apartments , and classify four activities - no movement , standing up , sitting down , and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response ( IIR ) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification .
We develop algorithms to autonomously identify sensor contexts and propose a gating function to automatically activate the most accurate machine learning model among the set of shared expert models . We assess the performance of Share-n-Learn for activity recognition when a dynamic sensor constantly migrates from one body location to another . Our analysis based on real data collected with human subjects on three datasets demonstrates that Share-n-Learn achieves , on average , 68.4 % accuracy in detecting physical activities with context-varying wearables .
Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift . In this work , we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives , including architecture , algorithm design and system implementation . First , the Edge computing architecture is introduced and several critical management tasks are also investigated .
In this paper , we propose a framework , based on the notion of online learning , for real-time and on-device machine learning training . We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers . Our results , based on Pegasos online learning , demonstrate that the proposed approach achieves 97 % accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40 % .
This is yet to be explored . In order to investigate this question , in this paper , we explore how these various motion sensors behave in different situations in the activity recognition process . For this purpose , we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions .
The dataset comprises of RGB-D images , point cloud data , automatically generated skeleton tracks in addition to crowdsourced annotations . We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking .
These researchers need a simulator that can synthesize realistic sensory datasets . To solve this problem , we propose the ' UbiSim ' simulator for activity recognition research . UbiSim provides a 3D graphical user interface to enable spatial perception using multiple sensors , including those that detect motion , pressure , vibration , temperature , and contact , along with RFID tags and receivers .
A variety of experiments have been conducted in the field , which include realistic scenarios of different types of walking/digging . The results of experiments show that an accuracy of more than 90 % and a false alarm of around 5 % can be achieved in real time for activity detection and recognition .
Thus , action potentials produced by neurons often constitute the only causal and observable correlate of dendritic processing . In order to remain true to the underlying biophysics of electrically excitable cells , we employ well-established mechanistic models of action potential generation to describe the nonlinear mapping of the aggregate current produced by the tree into an asynchronous sequence of spikes . Specific models of spike generation considered include conductance-based models such as Hodgkin-Huxley , Morris-Lecar , Fitzhugh-Nagumo , as well as simpler models of the integrate-and-fire and threshold-and-fire type .
The model unit is an analytical model ( S. Kalitzin et al. , Epilepsy Behav . 22 ( 2011 ) S102-S109 ) and does not correspond directly to realistic neuronal processes ( excitatory-inhibitory synaptic interactions , action potential generation ) . For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models .
One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the " building blocks of movement generation " , called motor primitives . Motor primitives , as used in this paper , are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties .
Different from the traditional robot action generation methods , our system generates the robot action and its parameters only from the HMM which is learned from the recognition phase . In this paper , it is a very important point that the robot can recognize and generate action using an identical HMM , and the robot do not need to record any trajectory data for the action generation using transitional method , such as Dynamic Movement Primitives ( DMPs ) . Since the robot action and its parameters are generated from HMMs , we can adjust the parameters to change the robot action speed .
The second aims at providing tools which facilitate video editing and enable to create interactive and structured video documents ( hyper-video ) . This last application includes the generation of actions when designating a pre-indexed area in the image . This is of particular interest for education , tourism , teleshopping .
The original work from which these structures are drawn has been carried out by researchers in a range of fields including anthropology , sociology and social psychology : the ' local approach ' described in this paper draws particularly on conversation analysis . We briefly discuss the application of heuristics derived from this work to the development of an interaction tracking system and , in more detail , discuss the use of this work in the development of an architecture for generating action for an interactive agent .
Describes the application of a domain-independent planner , COLLAGE , ( Co-Ordinated Localized aLgorithms for Action Generation and Execution ) , to the task of planning data-retrieval and data-preparation steps performed by Earth scientists . They describe the nature of this process , its inherent difficulties , the potential benefits of automation , and why AI-based planning is particularly suitable for carrying out this automation .
Translation is not only translating words from one language into another , but it is a universal and more complex process . The research question is : how do elements of the communication and translation process in MACS affect the action generation process within organisations ? As a result , the study proposes a communication model of MACS .
We first enhance a transition-based approach with additional actions that aim to handle particularities in the structure of the AMR . We analyze actions to address multi-aligned nodes and non-projective word orders , and explore several algorithms for action sequence generation , which incorporate the newly proposed actions . Secondly , we explore strategies for tackling AMR re-entrant concepts , which represent co-references in the associated textual data .
A benchmark evaluation of the state-of-the-art approaches for action recognition is also provided on our dataset . Furthermore , we propose an approach by integrating the NTS-Net into two-stream network so as to locate the most informative regions and extract more discriminative features for fine-grained action recognition . Our experiments show that the proposed approach significantly outperforms the existing approaches .
In order for action recognition to be useful in sports analytics a finer-grained action classification is needed . For this reason we focus on the fine-grained action recognition in tennis and explore the capabilities of deep neural networks for this task . In our model , videos are represented as sequences of features , extracted using the well-known Inception neural network , trained on an independent dataset .
Hand gestures are normally classified into multi-modal actions , including static gestures , fine-grained dynamic gestures , and coarse-grained dynamic gestures . Among them , the fine-grained action detection is limited under the small-scale image region condition . To solve this problem , we propose the HandSense , a new system for the multi-modal HGR based on a combined RGB and depth cameras to improve the fine-grained action descriptors as well as preserve the ability to perform general action recognition .
Secondly , the inferred hand/object posi- tion and interaction status are utilized to provide 1 ) more compact feature pooling by effectively pruning large num- ber of motion features from irrelevant spatio-temporal po- sitions and 2 ) discriminative action detection by a granu- larity fusion strategy . Comprehensive experiments on two challenging fine-grained activity datasets ( i.e. , cooking ac- tion ) show that the proposed framework achieves high ac- curacy/robustness in tracking multiple mutually occluded hands/objects during manipulation as well as significant performance improvement on fine-grained action detection over state-of-the-art methods .
Recent research in the area of action recognition has focused on coarse-grained action recognition , and there have been few studies on fine-grained action recognition . In response to this phenomenon , we propose a method for fine-grained action recognition using a deep convolutional network . This method uses the I3D network , which has achieved great success in the area of coarse-grained action recognition , as the basic network architecture . At the same time , the human pose and hand are extracted for obtaining local features of the fine-grained action .
We describe a DNN for fine-grained action classification and video captioning . It gives state-of-the-art performance on the challenging Something-Something dataset , with over 220 , 000 videos and 174 fine-grained actions .
In this paper , we develop an effective method of detecting and tracking hands in uncontrolled videos based on multiple cues including hand shape , skin color , upper body position and flow information . We apply our hand detection results to perform fine-grained human action recognition . We demonstrate that motion features extracted from hand areas can help classify actions even when they look familiar and they are associated with visually similar objects .
Abstract This paper addresses the problems of both general and also fine-grained human action recognition in video sequences . Compared with general human actions , fine-grained action information is more difficult to detect and occupies relatively small-scale image regions .
While several computer aided FoG event detection methods have been proposed , they were not particularly designed for video data collected during clinical diagnosis . In this paper , we treat video based FoG detection as a fine grained human action recognition problem and reduce the interference of visual content which is irrelevant to FoG , such as gait motion of supporting staff involved in clinical assessment . In order to effectively characterize FoG patterns , we propose to identify anatomic patches as the candidate regions which could be relevant to FoG events , and formulate FoG detection as a weakly-supervised learning task .
On the other hand , for some well-trained traditional supervised learning models , it is a waste of time to collect enough samples of new categories and retrain them together in order to identify new categories . We proposes a mapping between visual features of video and semantic description of fine-grained human action recognition . Unlike most current zero-shot learning methods , which use manual features as visual features , we uses features learnt from I3D network model as visual features , which are more general than manual features .
When performed automatically by computers , it has many practical applications , such as in human - robot interaction , human behaviour analysis and image descriptions for the visually impaired . Furthermore , detailed part information has been shown to be beneficial in other visual recognition tasks such as fine - grained recognition [ reference ] , human pose estimation [ reference ] and object detection [ reference ] .
section : Conclusion We have proposed a system for joint object detection and part localization capable of state - of - the - art fine - grained object recognition . Our method learns detectors and part models and enforces learned geometric constraints between parts and with the object frame .
The integration of unlabeled data regularizes the CNN learning process . We show that the LSRO method is superior to the two available strategies for dealing with unlabeled data ; and a demonstration that the proposed semi - supervised pipeline has a consistent improvement over the ResNet baseline on three person re - ID datasets and one fine - grained recognition dataset . section : Related Work
An action can be represented as a sequence of salient postures . Effective modeling of the salient postures is critical for robust action recognition . This paper proposes to characterize the salient postures using a set of the spatio-temporal interesting points ( STIPs ) .
In this paper , we propose a novel temporal spiking recurrent neural network ( TSRNN ) to perform robust action recognition in videos . The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals .
We use structure from motion with the physical scale and gravity direction as an initialization of our bundle adjustment for force estimation . Our method shows quantitatively equivalent reconstruction comparing to IMU measurements in terms of gravity and scale recovery and outperforms method based on 2D optical flow for an active action recognition task . We apply our method to first person videos of mountain biking , urban bike racing , skiing , speedflying with parachute , and wingsuit flying where inertial measurements are not accessible .
Evaluation shows that the proposed features are discriminative and computationally efficient . Our method achieves state-of-the-art performance with the standard human action recognition benchmarks , namely KTH and Weizmann datasets .
During classification , we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence . The proposed approach achieves state-of-the-art performance with standard human action recognition benchmarks , namely KTH and UCF-sports datasets and competitive results for the Hollywood ( HOHA ) dataset .
But the high computational complexity of optical flow hinders these methods from applying to realtime scenarios . In this paper , we advance an efficient camera motion estimation and compensation method for realtime action recognition by exploiting motion vectors in video compressed-domain ( a.k.a . compressed-domain global motion estimation , CGME ) .
Viewpoint variation is a major challenge in video- based human action recognition . We exploit the simultaneous RGB and Depth sensing of RGB-D cameras to address this problem .
We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure : the addition problem , the multiplication problem and the MNIST classification problem using sequence of pixels . In addition , we present results for a benchmark action recognition problem .
Finally , we learn a classification matrix by iterative optimization . Empirical studies on benchmark tasks of action recognition demonstrate that our algorithm can improve classification accuracy significantly .
Motivated by the idea that domain knowledge is of great concern in sports field , we propose a keyframe based temporal segment network ( KTSN ) for classification and achieve remarkable performance . Experimental results demonstrate that FSD-10 is an ideal dataset for benchmarking action recognition algorithms , as it requires to accurately extract action motions rather than action poses . We hope FSD-10 , which is designed to have a large collection of finegrained actions , can serve as a new challenge to develop more robust and advanced action recognition models .
In this paper , we propose a novel spatio-temporal feature which is useful for feature-fusion-based action recognition with Multiple Kernel Learning ( MKL ) . The proposed spatio-temporal feature is based on moving SURF interest points grouped by Delaunay triangulation and on their motion over time .
The analysis is characterized by working on sub-sequences of the entire video signals , processing a small fragment of the whole image , estimating the location of the region of interest , using simple operations ( sum , subtraction , multiplications , divisions ) , extracting a reduced number of features per frame ( 6 features ) , and using a combination of four linear classifiers ( one perceptron and three support vector machines ) with a hierarchical structure . The method is evaluated on two of the datasets cited in the human action recognition literature , the Weizmann and the UIUC datasets . Results show that for the case of the Weizmann dataset , the correct classification rate ( CCR ) is 99.95 % when the LOOCV Protocol is used and 98.38 % for the case of Protocol 60 - 40 , which is comparable or even higher than that of current state-of-the-art methods .
Activity Recognition and Prediction in Real Homes Share-n-Learn Enabling Edge Intelligence for Activity Recognition in Smart Homes Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification Fusion of Smartphone Motion Sensors for Physical Activity Recognition Complex and Long Activities Dataset UbiSim: Multiple Sensors Mounted Smart House Simulator Development Real-time activity recognition from seismic signature via multi-scale symbolic time series analysis (MSTSA) Identification of Dendritic Processing in Spiking Neural Circuits Multiple oscillatory States in Models of Collective neuronal Dynamics Reinforcement Learning for Parameterized Motor Primitives Learning by showing: An end-to-end imitation leaning approach for robot action recognition and generation Spatio-Temporal Segmentation and General Motion Characterization for Video Indexing and Retrieval Interactional Structure Applied to the Identification and Generation of Visual Interactive Behavior: Robots that (Usually) Follow the Rules AI-based planning for data analysis tasks [for Earth sciences] Modelling communication processes in management accounting and control systems Enhancements on a Transition-Based Approach for AMR Parsing Using LSTM Networks Fine-Grained Action Recognition on a Novel Basketball Dataset Deep Learning for Domain-Specific Action Recognition in Tennis HandSense: smart multimodal hand gesture recognition based on deep neural networks Multiple Granularity Analysis for Fine-Grained Action Detection Multi-stream I3D Network for Fine-grained Action Recognition Fine-grained Video Classification and Captioning Hand Detection and Tracking in Videos for Fine-Grained Action Recognition Region-sequence based six-stream CNN features for general and fine-grained human action recognition in videos Vision-Based Freezing of Gait Detection with Anatomic Patch Based Representation Fine-grained Human Action Recognition Based on Zero-Shot Learning Holistic, Instance-Level Human Parsing Part-Based R-CNNs for Fine-Grained Category Detection Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Salient Posture Modeling Based on Spatio-temporal Interesting Points Temporal Spiking Recurrent Neural Network for Action Recognition Force from Motion: Decoding Physical Sensation in a First Person Video Action recognition based on sparse motion trajectories Action recognition in video using a spatial-temporal graph-based feature representation Compressed-Domain Based Camera Motion Estimation for Realtime Action Recognition Viewpoint Invariant RGB-D Human Action Recognition Improving performance of recurrent neural network with relu nonlinearity An Unsupervised Domain Adaptation Algorithm Based on Canonical Correlation Analysis FSD-10: A Dataset for Competitive Sports Content Analysis A SURF-Based Spatio-Temporal Feature for Feature-Fusion-Based Action Recognition A human action recognition approach with a novel reduced feature set based on the natural domain knowledge of the human figure
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MASS DASC ACC IJCNN ROBIO ICCP ICASSP CVPR Workshops CVPR ACCV Workshops (1) ACCV (1) BMVC ECCV 2017 IEEE International Conference on Computer Vision (ICCV) CVPR ICIP AVSS PCM (1) DICTA CCCV (3) ECCV Workshops (1)
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