After the mapping of SOM , a human action sequence is represented as a trajectory of map units . For action recognition , a longest common subsequence algorithm is utilized to match action trajectories on the map robustly . The experiments are carried out on a well known human action dataset , viz . We evaluate the proposed method on the standard G3D and UTKinect-Action datasets consisting of five and ten actions , respectively . Our results demonstrate that the proposed approach performs well for action detection and recognition under different temporal scales , and is able to outperform the state of the art methods at low latency . This paper proposes a new visual framework for action recognition in videos , that consists of an energy detector coupled with a carefully designed multiband energy based filterbank . The tracking of video energy is performed using perceptually inspired 3D Gabor filters combined with ideas from Dominant Energy Analysis . Recent years have witnessed the popularity of using recurrent neural network ( RNN ) for action recognition in videos . However , videos are of high dimensionality and contain rich human dynamics with various motion scales , which makes the traditional RNNs difficult to capture complex action information . Human activities , despite their unstructured nature , tend to have a natural hierarchical structure ; for instance , generally making a coffee involves a three-step process of turning on the coffee machine , putting sugar in cup and opening the fridge for milk . Action sequence recognition is then handled using a discriminative Hidden Markov Model ( HMM ) . RADiaL , a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM , has been built and made publicly available . To the best of our knowledge , this is the first attempt to predict temporal images , dynamic patterns of a still image , to alleviate the lack of motion information in still images . Extensive experiments on four benchmarks demonstrate the positive effect of temporal image prediction on the accuracy of still image action recognition , outperforming the state-of-the-art methods in the literature . Furthermore , a robust classifier-based method is proposed to estimate DTVD robustly , and to efficiently represent these sets . D3 is evaluated in action and image recognition tasks . It achieves excellent robustness , accuracy and speed . conLSH reduces the search space to approximately 42 % of the entire dataset , when compared with an exhaustive search by the aforementioned FOGSAA , Bag of Words method and the standard LSH implementations . Secondly , the effectiveness of conLSH is demon- strated in action recognition of the video clips , which yields an average gain of 12.83 % in terms of classification accuracy over the state of the art methods using STIP descriptors . The last but of great significance is that this article provides a way of automatically annotating long and composite real life videos . Applied from human ’s outstanding ability of perception , the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns . This paper presents a biological process based action recognition model of computer vision , which is inspired from visual information processing of human brain for action recognition of visual sequences . Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain . Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning . To result crisp flow boundaries , LiteFlowNet regularizes flow field through feature - driven local convolution ( f - lcon ) . With its lightweight , accurate , and fast flow computation , we expect that LiteFlowNet can be deployed to many applications such as motion segmentation , action recognition , SLAM , 3D reconstruction and more . Acknowledgement . With the help of active depth-aware sensors , such as Kinect from Microsoft and SwissRanger , the depth map can be captured at the video frame rate together with color information to enable the real-time reconstruction . Particularly , it features prominently in the activity recognition and remote rehabilitation . Unfortunately , the coarseness of the depth map make it difficult to extract the detailed information in 3D reconstruction of the scene and tracking of thin objects . This paper presents an efficient hybrid ( top-down and bottom-up ) framework for activity recognition based on analyzing group context in crowded scenes . The approach presented starts by discovering interacting groups of people using a graph based clustering algorithm . The assignment of sensor data to one from a possible set of predefined activities can essentially be considered as a classification task . In this study , we propose the application of a cluster-based classifier ensemble method to the activity recognition problem , as an alternative to single classification models . Experimental evaluation has been conducted on publicly available sensor data collected over a period of 26 days from a single person apartment . Signal segmentation is one of the most important processes in the activity recognition process . So far , windowing approaches is one of the commonly used segmentation technique to segment the data . Finally some activity recognition benchmarks are presented using current state-of-the-art techniques . 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 . Access to sensor datasets is essential for smart home research . For example , activity recognition research requires sensor datasets to assess the performance of activity recognition models and algorithms . There are several limitations that can prevent researchers from acquiring sensor datasets , such as cost , time , and inflexibility of smart homes . Continuously recorded videos also contain periods of inactivity , or activities that are not in the set of AoI , further complicating the problem of detection . This work attempts to overcome these challenges by introducing the concepts of ( 1 ) discriminative descriptors , ( 2 ) a goal for the AoI , represented by the most discriminative descriptors , ( 3 ) a novel , goal-based framework for activity detection and recognition in video . We represent AoI goals by descriptors found to be the most discriminative , as defined by a function of their correlation distance from the majority of the data , rather than by semantics or parametric modeling . The use of simulated environments may facilitate rapid generation of comprehensive datasets with minimal cost . This paper introduces an approach to the simulation of thermal sensor data for activity detection and recognition . The approach utilizes multi-touch interfaces to facilitate intuitive recordings of a range of scenarios and supports deployment to a range of mobile platforms . Wearable inertial measurement units incorporating accelerometers and gyroscopes are increasingly used for activity analysis and recognition . In this paper an activity classification algorithm is presented which includes a novel multi-step refinement with the aim of improving the classification accuracy of traditional approaches . In this paper , we propose a new semantic model describing trajectories in multi-floor environment , and then N-gram model is implemented for transferring trajectory to human activity knowledge . Our method successfully alleviates the common problem of indoor movement representation and activity recognition accuracy affected by wireless signal calibration . Experimental implementation and analysis on both real and synthetic dataset exhibit that our proposed method can effectively process with indoor movement , and it renders good performance in accuracy and robustness for activity recognition with less calibration effort . In practice , we often have a set of labeled training instances ready for an activity recognition task . If we can transfer such knowledge to a new activity recognition scenario that is different from , but related to , the source domain , it will ease our effort to perform manual labeling of training data for the new scenario . In this paper , we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in activity recognition problem . The proposed extraction procedure is demonstrated using trajectory data collected from volunteers . Test results show that the proposed sliding-window Euclidean distance segmentation approach has error rates as low as 2–4 % ; recognition tests suggest that SVM performs the best in travel-mode recognition and MLP performs the best in activity-type recognition ; feature selection tests reveal that the methods with feature selection achieve higher accuracy than those with total features . Overall , the proposed trip chain extraction procedure achieves an average completion rate above 80 % . section : Introduction Accurate 3D hand and human pose estimation is an important requirement for activity recognition with diverse applications , such as human - computer interaction or augmented reality [ reference ] . It has been studied for decades in computer vision community and has attracted considerable research interest again due to the introduction of low - cost depth cameras . section : Introduction Localizing body parts for human body is a fundamental yet challenging task in computer vision , and it serves as an important basis for high - level vision tasks , , activity recognition , clothing parsing , human re - identification , and human - computer interaction . Achieving accurate localization , however , is difficult due to the highly articulated human body limbs , occlusion , change of viewpoint , and foreshortening . Objectives : To provide comprehensive review of different group activity recognition methods , categorize them and provide path to new researcher in this domain . Methods/Statistical Analysis : Different methods of group activity recognition categorized and analyzed according to hand-crafted and learned feature descriptors . Pros and cons of each method are presented . Human group activity recognition is still a challenging task in computer vision . However , most of the works focus on the feature extraction and the analysis of the motion trajectories for recognizing the multi-person or group activities in surveillance videos . Human activity recognition using mobile sensors is becoming increasingly important . Scaling up from individuals to groups , that is , Group Activity Recognition ( GAR ) , has attracted significant attention recently . This paper investigates energy consumption for GAR and proposes a novel distributed middleware called GroupSense for mobile GAR . Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important . Scaling up from individuals to groups , that is , Group Activity Recognition ( GAR ) , has attracted significant attention recently . This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR . We build a deep model to capture these dynamics based on LSTM ( long short-term memory ) models . To make use of these observations , we present a 2-stage deep temporal model for the group activity recognition problem . In our model , a LSTM model is designed to represent action dynamics of individual people in a sequence and another LSTM model is designed to aggregate person-level information for whole activity understanding . Group activity recognition indoors has gained little attention , but remains of paramount importance , such as working together , taking classes , and discussions . In this paper , we propose a framework for group activity detection and recognition ( i.e. , GADAR framework ) using smartphone sensors and Bluetooth beacons data . This framework consists of the following four layers : user layer , data package layer , processing layer , and output layer . An experiment was conducted to identify the following three common group activities : taking class , seminar , and discussion . The result shows that the proposed GADAR framework could provide more than 89 % accuracy in group detection and 89 % accuracy in recognizing group activity . Small group people activity recognition has attracted much attention in computer vision community in recent years , since it has great potential in public security applications . Comparing to single human activity recognition , group human activity recognition has much more challenges , such as mutual occlusions between different people , the varying group size , and the interaction within or between groups . In this paper , we propose a novel structural feature set to represent group behavior as well as a probabilistic framework for group activity learning and recognition . In this paper , we present an approach that exploits local group relationship to tackle the human group activity recognition problem . Specifically , rather than analyze every human motion , we first grouping individual human object into local groups to represent the relationship in the overall scene . The main purpose of this survey is to broadly categorize and analyse GAR according to handcrafted features based on machine learning model and learned features based on deep model . Various GAR models illustrated by considering activities of individual person , person-to-person interaction , person-to-group interaction and group interaction using temporal sequence information from video frames for recognition of group activity are discussed . The review facilitates in diverse applications , and the models described in different applications present specifically in surveillance , sport analytics , video summary , etc . Moreover , we guide the networks to transfer the knowledge across the weight matrices by designing a task-specific loss function , so that the relation information is well preserved during transfer . We conduct experiments on two different scenarios for video analysis , including a new proposed setting for unsupervised skeleton-based action recognition across different datasets , and supervised group activity recognition with multi-modal inputs . Extensive experiments on six widely used datasets illustrate that our GINs achieve very competitive performance in comparison with the state-of-the-arts . With the increasing availability of wearable devices , research on egocentric activity recognition has received much attention recently . In this paper , we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data of 20 fine-grained and diverse activity categories . Our approach significantly outperforms the state-of-the-art method on the public ADL dataset ( i.e. , 36.8 % to 46.7 % ) and our ADL+ dataset ( i.e. , 32.1 % to 60.0 % ) in terms of classification accuracy . In addition , we also conduct a series of analyses to explore relative merits of each modality to egocentric activity recognition . Specifically , we deploy a Cauchy estimator to maximize the correlations of the sharable components and enforce the orthogonality constraints on the distinctive components to guarantee their high independencies . Since the egocentric action recognition is usually sensitive to hand poses , we extend our MDNN by integrating with the hand cues to enhance the recognition accuracy . Extensive experimental results on a newly collected data set and two additional benchmarks are presented to demonstrate the effectiveness of our proposed method for RGB-D egocentric action recognition . The result is a compact representation with superior performance . In addition , we provide the first systematic evaluation of motion , object and egocentric cues in egocentric action recognition . Our benchmark leads to several surprising findings . In this paper , we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem , using both the video and sensor data . First , we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos . Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature . In this work , we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning ( MKL ) and multi-kernel boosting ( MKBoost ) . For that purpose , firstly grid optical-flow , virtual-inertia feature , log-covariance , cuboid are extracted from the video . Abstract Egocentric activity recognition has recently generated great popularity in computer vision due to its widespread applications in egocentric video analysis . However , it poses new challenges comparing to the conventional third-person activity recognition tasks , which are caused by significant body shaking , varied lengths , and poor recoding quality , etc . Recent advances in automated video- and multimodal-data-based action and activity recognition have made it possible to reduce the extent of manual intervention required to effectively carry out process supervision tasks . In this paper , we propose algorithms for automated egocentric human action and activity recognition from multimodal data , with a target application of monitoring and assisting a user perform a multistep medical procedure . We propose a supervised deep multimodal fusion framework that relies on concurrent processing of motion data acquired with wearable sensors and video data acquired with an egocentric or body-mounted camera . Both modules are seamlessly integrated for collaborative prediction . Extensive experimental results on egocentric activity prediction as well as recognition well demonstrate the effectiveness of the proposed method . Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective . In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly , without further use of visual features . Initially , we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions . A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition . Detecting and recognizing objects interacting with humans lie in the center of first-person ( egocentric ) daily activity recognition . However , due to noisy camera motion and frequent changes in viewpoint and scale , most of the previous egocentric action recognition methods fail to capture and model highly discriminative object features . Egocentric activity recognition ( EAR ) is an emerging area in the field of computer vision research . Motivated by the current success of Convolutional Neural Network ( CNN ) , we propose a multi-stream CNN for multimodal egocentric activity recognition using visual ( RGB videos ) and sensor stream ( accelerometer , gyroscope , etc . ) . In order to effectively capture the spatio-temporal information contained in RGB videos , two types of modalities are extracted from visual data : This paper presents a novel approach for real-time egocentric activity recognition in which component atomic events are characterised in terms of binary relationships between parts of the body and manipulated objects . The key contribution is to summarise , within a histogram , the relationships that hold over a fixed time interval .
After the mapping of SOM , a human action sequence is represented as a trajectory of map units . For action recognition , a longest common subsequence algorithm is utilized to match action trajectories on the map robustly . The experiments are carried out on a well known human action dataset , viz .
We evaluate the proposed method on the standard G3D and UTKinect-Action datasets consisting of five and ten actions , respectively . Our results demonstrate that the proposed approach performs well for action detection and recognition under different temporal scales , and is able to outperform the state of the art methods at low latency .
This paper proposes a new visual framework for action recognition in videos , that consists of an energy detector coupled with a carefully designed multiband energy based filterbank . The tracking of video energy is performed using perceptually inspired 3D Gabor filters combined with ideas from Dominant Energy Analysis .
Recent years have witnessed the popularity of using recurrent neural network ( RNN ) for action recognition in videos . However , videos are of high dimensionality and contain rich human dynamics with various motion scales , which makes the traditional RNNs difficult to capture complex action information .
Human activities , despite their unstructured nature , tend to have a natural hierarchical structure ; for instance , generally making a coffee involves a three-step process of turning on the coffee machine , putting sugar in cup and opening the fridge for milk . Action sequence recognition is then handled using a discriminative Hidden Markov Model ( HMM ) . RADiaL , a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM , has been built and made publicly available .
To the best of our knowledge , this is the first attempt to predict temporal images , dynamic patterns of a still image , to alleviate the lack of motion information in still images . Extensive experiments on four benchmarks demonstrate the positive effect of temporal image prediction on the accuracy of still image action recognition , outperforming the state-of-the-art methods in the literature .
Furthermore , a robust classifier-based method is proposed to estimate DTVD robustly , and to efficiently represent these sets . D3 is evaluated in action and image recognition tasks . It achieves excellent robustness , accuracy and speed .
conLSH reduces the search space to approximately 42 % of the entire dataset , when compared with an exhaustive search by the aforementioned FOGSAA , Bag of Words method and the standard LSH implementations . Secondly , the effectiveness of conLSH is demon- strated in action recognition of the video clips , which yields an average gain of 12.83 % in terms of classification accuracy over the state of the art methods using STIP descriptors . The last but of great significance is that this article provides a way of automatically annotating long and composite real life videos .
Applied from human ’s outstanding ability of perception , the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns . This paper presents a biological process based action recognition model of computer vision , which is inspired from visual information processing of human brain for action recognition of visual sequences . Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain .
Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning .
To result crisp flow boundaries , LiteFlowNet regularizes flow field through feature - driven local convolution ( f - lcon ) . With its lightweight , accurate , and fast flow computation , we expect that LiteFlowNet can be deployed to many applications such as motion segmentation , action recognition , SLAM , 3D reconstruction and more . Acknowledgement .
With the help of active depth-aware sensors , such as Kinect from Microsoft and SwissRanger , the depth map can be captured at the video frame rate together with color information to enable the real-time reconstruction . Particularly , it features prominently in the activity recognition and remote rehabilitation . Unfortunately , the coarseness of the depth map make it difficult to extract the detailed information in 3D reconstruction of the scene and tracking of thin objects .
This paper presents an efficient hybrid ( top-down and bottom-up ) framework for activity recognition based on analyzing group context in crowded scenes . The approach presented starts by discovering interacting groups of people using a graph based clustering algorithm .
The assignment of sensor data to one from a possible set of predefined activities can essentially be considered as a classification task . In this study , we propose the application of a cluster-based classifier ensemble method to the activity recognition problem , as an alternative to single classification models . Experimental evaluation has been conducted on publicly available sensor data collected over a period of 26 days from a single person apartment .
Signal segmentation is one of the most important processes in the activity recognition process . So far , windowing approaches is one of the commonly used segmentation technique to segment the data .
Finally some activity recognition benchmarks are presented using current state-of-the-art techniques . 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 .
Access to sensor datasets is essential for smart home research . For example , activity recognition research requires sensor datasets to assess the performance of activity recognition models and algorithms . There are several limitations that can prevent researchers from acquiring sensor datasets , such as cost , time , and inflexibility of smart homes .
Continuously recorded videos also contain periods of inactivity , or activities that are not in the set of AoI , further complicating the problem of detection . This work attempts to overcome these challenges by introducing the concepts of ( 1 ) discriminative descriptors , ( 2 ) a goal for the AoI , represented by the most discriminative descriptors , ( 3 ) a novel , goal-based framework for activity detection and recognition in video . We represent AoI goals by descriptors found to be the most discriminative , as defined by a function of their correlation distance from the majority of the data , rather than by semantics or parametric modeling .
The use of simulated environments may facilitate rapid generation of comprehensive datasets with minimal cost . This paper introduces an approach to the simulation of thermal sensor data for activity detection and recognition . The approach utilizes multi-touch interfaces to facilitate intuitive recordings of a range of scenarios and supports deployment to a range of mobile platforms .
Wearable inertial measurement units incorporating accelerometers and gyroscopes are increasingly used for activity analysis and recognition . In this paper an activity classification algorithm is presented which includes a novel multi-step refinement with the aim of improving the classification accuracy of traditional approaches .
In this paper , we propose a new semantic model describing trajectories in multi-floor environment , and then N-gram model is implemented for transferring trajectory to human activity knowledge . Our method successfully alleviates the common problem of indoor movement representation and activity recognition accuracy affected by wireless signal calibration . Experimental implementation and analysis on both real and synthetic dataset exhibit that our proposed method can effectively process with indoor movement , and it renders good performance in accuracy and robustness for activity recognition with less calibration effort .
In practice , we often have a set of labeled training instances ready for an activity recognition task . If we can transfer such knowledge to a new activity recognition scenario that is different from , but related to , the source domain , it will ease our effort to perform manual labeling of training data for the new scenario . In this paper , we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in activity recognition problem .
The proposed extraction procedure is demonstrated using trajectory data collected from volunteers . Test results show that the proposed sliding-window Euclidean distance segmentation approach has error rates as low as 2–4 % ; recognition tests suggest that SVM performs the best in travel-mode recognition and MLP performs the best in activity-type recognition ; feature selection tests reveal that the methods with feature selection achieve higher accuracy than those with total features . Overall , the proposed trip chain extraction procedure achieves an average completion rate above 80 % .
section : Introduction Accurate 3D hand and human pose estimation is an important requirement for activity recognition with diverse applications , such as human - computer interaction or augmented reality [ reference ] . It has been studied for decades in computer vision community and has attracted considerable research interest again due to the introduction of low - cost depth cameras .
section : Introduction Localizing body parts for human body is a fundamental yet challenging task in computer vision , and it serves as an important basis for high - level vision tasks , , activity recognition , clothing parsing , human re - identification , and human - computer interaction . Achieving accurate localization , however , is difficult due to the highly articulated human body limbs , occlusion , change of viewpoint , and foreshortening .
Objectives : To provide comprehensive review of different group activity recognition methods , categorize them and provide path to new researcher in this domain . Methods/Statistical Analysis : Different methods of group activity recognition categorized and analyzed according to hand-crafted and learned feature descriptors . Pros and cons of each method are presented .
Human group activity recognition is still a challenging task in computer vision . However , most of the works focus on the feature extraction and the analysis of the motion trajectories for recognizing the multi-person or group activities in surveillance videos .
Human activity recognition using mobile sensors is becoming increasingly important . Scaling up from individuals to groups , that is , Group Activity Recognition ( GAR ) , has attracted significant attention recently . This paper investigates energy consumption for GAR and proposes a novel distributed middleware called GroupSense for mobile GAR .
Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important . Scaling up from individuals to groups , that is , Group Activity Recognition ( GAR ) , has attracted significant attention recently . This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR .
We build a deep model to capture these dynamics based on LSTM ( long short-term memory ) models . To make use of these observations , we present a 2-stage deep temporal model for the group activity recognition problem . In our model , a LSTM model is designed to represent action dynamics of individual people in a sequence and another LSTM model is designed to aggregate person-level information for whole activity understanding .
Group activity recognition indoors has gained little attention , but remains of paramount importance , such as working together , taking classes , and discussions . In this paper , we propose a framework for group activity detection and recognition ( i.e. , GADAR framework ) using smartphone sensors and Bluetooth beacons data . This framework consists of the following four layers : user layer , data package layer , processing layer , and output layer .
An experiment was conducted to identify the following three common group activities : taking class , seminar , and discussion . The result shows that the proposed GADAR framework could provide more than 89 % accuracy in group detection and 89 % accuracy in recognizing group activity .
Small group people activity recognition has attracted much attention in computer vision community in recent years , since it has great potential in public security applications . Comparing to single human activity recognition , group human activity recognition has much more challenges , such as mutual occlusions between different people , the varying group size , and the interaction within or between groups . In this paper , we propose a novel structural feature set to represent group behavior as well as a probabilistic framework for group activity learning and recognition .
In this paper , we present an approach that exploits local group relationship to tackle the human group activity recognition problem . Specifically , rather than analyze every human motion , we first grouping individual human object into local groups to represent the relationship in the overall scene .
The main purpose of this survey is to broadly categorize and analyse GAR according to handcrafted features based on machine learning model and learned features based on deep model . Various GAR models illustrated by considering activities of individual person , person-to-person interaction , person-to-group interaction and group interaction using temporal sequence information from video frames for recognition of group activity are discussed . The review facilitates in diverse applications , and the models described in different applications present specifically in surveillance , sport analytics , video summary , etc .
Moreover , we guide the networks to transfer the knowledge across the weight matrices by designing a task-specific loss function , so that the relation information is well preserved during transfer . We conduct experiments on two different scenarios for video analysis , including a new proposed setting for unsupervised skeleton-based action recognition across different datasets , and supervised group activity recognition with multi-modal inputs . Extensive experiments on six widely used datasets illustrate that our GINs achieve very competitive performance in comparison with the state-of-the-arts .
With the increasing availability of wearable devices , research on egocentric activity recognition has received much attention recently . In this paper , we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data of 20 fine-grained and diverse activity categories .
Our approach significantly outperforms the state-of-the-art method on the public ADL dataset ( i.e. , 36.8 % to 46.7 % ) and our ADL+ dataset ( i.e. , 32.1 % to 60.0 % ) in terms of classification accuracy . In addition , we also conduct a series of analyses to explore relative merits of each modality to egocentric activity recognition .
Specifically , we deploy a Cauchy estimator to maximize the correlations of the sharable components and enforce the orthogonality constraints on the distinctive components to guarantee their high independencies . Since the egocentric action recognition is usually sensitive to hand poses , we extend our MDNN by integrating with the hand cues to enhance the recognition accuracy . Extensive experimental results on a newly collected data set and two additional benchmarks are presented to demonstrate the effectiveness of our proposed method for RGB-D egocentric action recognition .
The result is a compact representation with superior performance . In addition , we provide the first systematic evaluation of motion , object and egocentric cues in egocentric action recognition . Our benchmark leads to several surprising findings .
In this paper , we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem , using both the video and sensor data . First , we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos .
Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature . In this work , we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning ( MKL ) and multi-kernel boosting ( MKBoost ) . For that purpose , firstly grid optical-flow , virtual-inertia feature , log-covariance , cuboid are extracted from the video .
Abstract Egocentric activity recognition has recently generated great popularity in computer vision due to its widespread applications in egocentric video analysis . However , it poses new challenges comparing to the conventional third-person activity recognition tasks , which are caused by significant body shaking , varied lengths , and poor recoding quality , etc .
Recent advances in automated video- and multimodal-data-based action and activity recognition have made it possible to reduce the extent of manual intervention required to effectively carry out process supervision tasks . In this paper , we propose algorithms for automated egocentric human action and activity recognition from multimodal data , with a target application of monitoring and assisting a user perform a multistep medical procedure . We propose a supervised deep multimodal fusion framework that relies on concurrent processing of motion data acquired with wearable sensors and video data acquired with an egocentric or body-mounted camera .
Both modules are seamlessly integrated for collaborative prediction . Extensive experimental results on egocentric activity prediction as well as recognition well demonstrate the effectiveness of the proposed method .
Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective . In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly , without further use of visual features . Initially , we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions .
A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition . Detecting and recognizing objects interacting with humans lie in the center of first-person ( egocentric ) daily activity recognition . However , due to noisy camera motion and frequent changes in viewpoint and scale , most of the previous egocentric action recognition methods fail to capture and model highly discriminative object features .
Egocentric activity recognition ( EAR ) is an emerging area in the field of computer vision research . Motivated by the current success of Convolutional Neural Network ( CNN ) , we propose a multi-stream CNN for multimodal egocentric activity recognition using visual ( RGB videos ) and sensor stream ( accelerometer , gyroscope , etc . ) . In order to effectively capture the spatio-temporal information contained in RGB videos , two types of modalities are extracted from visual data :
This paper presents a novel approach for real-time egocentric activity recognition in which component atomic events are characterised in terms of binary relationships between parts of the body and manipulated objects . The key contribution is to summarise , within a histogram , the relationships that hold over a fixed time interval .
Human action recognition based on Self Organizing Map Scale Invariant Human Action Detection from Depth Cameras Using Class Templates Advances on action recognition in videos using an interest point detector based on multiband spatio-temporal energies Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos HMM-based Activity Recognition with a Ceiling RGB-D Camera TICNN: A Hierarchical Deep Learning Framework for Still Image Action Recognition Using Temporal Image Prediction Representing Sets of Instances for Visual Recognition An Improved Video Analysis using Context based Extension of LSH A Bio-Inspired Modeling of Visual Information Processing for Action Recognition Res2Net: A New Multi-scale Backbone Architecture LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation A Confidence Weighted Real-Time Depth Filter for 3D Reconstruction A Hybrid Approach for Individual and Group Activity Analysis in Crowded Scene Application of a Cluster-Based Classifier Ensemble to Activity Recognition in Smart Homes Investigation on the Effect of Different Window Size in Segmentation for Common Sport Activity CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations A Smart Home Simulation Tool to Support the Recognition of Activities of Daily Living Mining discriminative descriptors for goal-based activity detection A Thermal Data Simulation Tool for the Testing of Novel Approaches to Activity Recognition A multi-level refinement approach towards the classification of quotidian activities using accelerometer data Human Activity Recognition with Trajectory Data in Multi-floor Indoor Environment Transfer Learning for Activity Recognition via Sensor Mapping Trip chain extraction using smartphone-collected trajectory data V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Learning Feature Pyramids for Human Pose Estimation A Comprehensive Study of Group Activity Recognition Methods in Video Qualitative Spatio-temporal Reasoning Based Group Activity Recognition Energy Considerations for Continuous Group Activity Recognition Using Mobile Devices: The Case of GroupSense GroupSense: Recognizing and Understanding Group Physical Activities using Multi-Device Embedded Sensing A Hierarchical Deep Temporal Model for Group Activity Recognition A framework for group activity detection and recognition using smartphone sensors and beacons Small group human activity recognition Local group relationship analysis for group activity recognition A Survey on Human Group Activity Recognition by Analysing Person Action from Video Sequences Using Machine Learning Techniques Graph Interaction Networks for Relation Transfer in Human Activity Videos Egocentric activity recognition with multimodal fisher vector Egocentric activity recognition by leveraging multiple mid-level representations Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition Delving into egocentric actions Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition Multi-modal Egocentric Activity Recognition using Audio-Visual Features Deep appearance and motion learning for egocentric activity recognition Deep Temporal Multimodal Fusion for Medical Procedure Monitoring Using Wearable Sensors Egocentric Activity Prediction via Event Modulated Attention Egocentric Hand Track and Object-Based Human Action Recognition Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition Multimodal Egocentric Activity Recognition Using Multi-stream CNN Egocentric Activity Monitoring and Recovery
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ICASSP CVPR Workshops ICIP ICPRAM ICIP AAAI IEEE transactions on pattern analysis and machine intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Intelligent Information Processing VISIGRAPP (Selected Papers) IWAAL IWAAL RSKT IJCAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) AINA CVPR ICIP ICME CVPR CVPR Workshops ECCV (2) ICVGIP ACCV (3)
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