Our approach can classify objects with different weights ( motor-related features ) and different surface frictions ( haptics-related features ) with high accuracy ( over 0.8 ) . The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities ; thus , this may represent a new effective way to perform activity recognition . The automated visual surveillance system will help to catch suspect in the scene , person identification in distance and re-identification , traffic management , sports , human computer interface , etc . Generally speaking , the human activity recognition in visual surveillance divided into following stages : object ( human or vehicle ) segmentation , feature extraction , object classification , object tracking , and activity recognition . Hence , the robust and object segmentation method in video camera is a very important phase because the rest of methods are strongly rely on it . We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition . Importantly , we extend the framework to incorporate hidden variables . Thus , the abnormal behaviour can be detected . The activity detection and recognition is performed using an affordable RGB-D camera . 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 . With the wide ranges of real-time event feed capturing devices , there has been significant progress in the area of digital image processing towards activity detection and recognition . Irrespective of the presence of various such devices , they are not adequate to meet dynamic monitoring demands of the visual surveillance system , and their features are highly limited towards complex human activity recognition system . We present a detailed analysis of our approach against competing methods and explore variants such as hierarchy depth and choice of non-linear feature function . Obtained results are comparable to state-of-the-art methods on three important activity recognition benchmarks with classification performance of 76.7 % mAP on Hollywood2 , 69.4 % on HMDB51 , and 93.6 % on UCF101 . In this paper we introduce a novel approach to classifier combination , which we term Weighted Ensemble Boosting . We apply the proposed algorithm to the problem of activity recognition in video , and compare its performance to different classifier combination methods . These include Approximate Bayesian Combination , Boosting , Feature Stacking , and the more traditional Sum and Product rules . We then show the generalizability of our neurobiologically-inspired integration framework by casting the GAI within this infrastructure . Since Motion and image analysis are both important for activity recognition in video , we also develop a new approach that extends the Hamiltonian Monte Carlo ( HMC ) to allow us to simultaneously search over the combined motion and image space in a concerted manner using well-known Markov Chain Monte Carlo ( MCMC ) techniques . For motion analysis in video , we use tracks generated from the video to calculate the Hamiltonian equations of motion for the systems under study , thus utilizing analytical Hamiltonian dynamics to derive a physically significant HMC algorithm which can be used for activity analysis . There is an increasing interest in activity recognition analysis due to the tremendous growth of sensors and devices that have recently brought significant attention to smart homes research which promotes inhabitants ’ comfort , security , and safety . Therefore , there is a necessity to have a comprehensive activity recognition approach which is not only limited to recognise human activities and to detect unknown/abnormal behaviors , but also could update a stream learning model in a real-time setting with sequential sensor data . We propose a method based on an augmented version of canonical correlation analysis ( AuCCA ) for linking high-dimensional features to activity-specific spatial displacements over time . We compare this continuous and discriminative approach to other well established methods in the field of activity recognition and detection . In particular , we first improve activity detections by incorporating temporal forward and backward mappings for regularization of detections . This paper presents a multisensor fusion framework for video activities recognition based on statistical reasoning and D-S evidence theory . Precisely , the framework consists in the combination of the events ’ uncertainty computation with the trained database and the fusion method based on the conflict management of evidences . Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition . Given the location of an arbitrary target of interest in the first frame of a video , the aim of visual object tracking is to estimate its position in all the subsequent frames [ reference ][ reference ][ reference ] . The task of estimating 3D human pose from 2D representations like monocular images or videos is an open research problem among the computer vision and graphics community for a long time . An understanding of human posture and limb articulation is important for high level computer vision tasks such as human action or activity recognition , sports analysis , augmented and virtual reality . A 2D representation of human pose , which is considered to be much easier to estimate , can be used for these tasks . This paper presents a novel Hierarchical Self-Attention Network ( HISAN ) to generate spatial-temporal tubes for action localization in videos . The essence of HISAN is to combine the two-stream convolutional neural network ( CNN ) with hierarchical bidirectional self-attention mechanism , which comprises of two levels of bidirectional self-attention to efficaciously capture both of the long-term temporal dependency information and spatial context information to render more precise action localization . The major drawback of such an approach is that the dictionary is learned in an unsupervised manner without considering the task ( classification or detection ) that follows it . In this work we develop task dependent(supervised ) dictionaries for action recognition and localization , i.e. , dictionaries that are best suited for the subsequent task . In the first part of the work , we propose a supervised max-margin framework for linear and non-linear Non-Negative Matrix Factorization ( NMF ) . Hence , to make the machine learning models easily deployable and application ready , it is highly desirable to curtail the dependence of learning algorithms on engineered features so that the construction of novel algorithms can be much faster . This thesis proposes a novel approach to represent a video as a graph for action recognition and localization using only class label information . In addition to that , this thesis also proposes a novel subspace attention mechanism to learn to capture long-range inter-dependencies in visual data . The ability to analyze the actions which occur in a video is essential for automatic understanding of sports . Action localization and recognition in videos are two main research topics in this context . In this chapter , we provide a detailed study of the prominent methods devised for these two tasks which yield superior results for sports videos . An action pose from a test frame can be segmented by GrabCut algorithm using the initial segmentation mask from the matched leaf node as the user labeling . Our approach does not rely on background subtraction , and enables action localization and recognition in realistic and challenging conditions ( such as crowded backgrounds ) . Experimental results show that our approach achieves start-of-art performances on the Weizmann dataset , CMU action dataset and UCF sports action dataset . We present a method for utilizing weakly supervised data for action localization in videos . We focus on sports video analysis , where videos contain scenes of multiple people . Difficulties are encountered in the application of existing methods when modeling temporal structures of videos . In the present study , we developed a novel method , referred to as Gemini Network , for effective modeling of temporal structures and achieving high-performance temporal action localization . The significant improvements afforded by the proposed method are attributable to three major factors . The loss of auxiliary classifiers increases the gradient signal that gets propagated back , and boosts our framework ’s performance . We achieve state-of-the-art performance for temporal action localization on two challenging temporal action localization datasets , THUMOS14 and ActivityNet . This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018 : temporal action proposals , temporal action localization , dense-captioning events in videos , trimmed action recognition , and spatio-temporal action localization . Our model is trained to simultaneously recognize and localize action classes in time and is based on two layer gated recurrent units ( GRU ) applied separately to two streams , i.e. appearance and optical flow streams . When used together with state-of-the-art person detection and tracking , our model is shown to improve substantially spatio-temporal action localization in videos . The gain is shown to be mainly due to improved temporal localization . To provide further details about the existing action recognition methods in this area , we decompose the action recognition framework into three main steps of feature extraction , dictionary learning to represent a video , and classification ; we overview several successful techniques for each of these steps . We also overview the problem of spatio-temporal localization of actions and argue that , in general , it manifests a more challenging problem compared to action recognition . We study several recent methods for action localization which have shown promising results on sports videos . We strive for spatio-temporal localization of actions in videos . The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations . Current state-of-the-art approaches usually extract these information with separate networks and use an extra mechanism for fusion to get detections . In this work , we present YOWO , a unified CNN architecture for real-time spatiotemporal action localization in video streams . YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation . For the integration , we introduce a Markov chain model which adds cues successively . The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization . The two contributions clearly improve the performance over respective baselines . This model seeks to fuse richer information of global video clip , short human attention and long-term human activity into a unified model . We have participated in two tasks : Task A , the Kinetics challenge and Task B , spatio-temporal action localization challenge . For Kinetics , we achieve 21.59 % error rate . The goal of this work is spatio-temporal action localization in videos , using only the supervision from video-level class labels . The state-of-the-art casts this weakly-supervised action localization regime as a Multiple Instance Learning problem , where instances are a priori computed spatio-temporal proposals . We propose TAL-Net , an improved approach to temporal action localization in video that is inspired by the Faster RCNN object detection framework . TAL-Net addresses three key shortcomings of existing approaches : ( 1 ) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations ; ( 2 ) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields ; and ( 3 ) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important . Temporal action proposal generation is an important yet challenging problem , since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content . This problem requires methods not only generating proposals with precise temporal boundaries , but also retrieving proposals to cover truth action instances with high recall and high overlap using relatively fewer proposals . Temporal action recognition always depends on temporal action proposal generation to hypothesize actions . Applications require temporal action proposal generation to handle both large video dataset and generate more potential actions and suffer from high computation cost due to the bottleneck of temporal action proposal generation . Temporal action proposal , which extracts segments of interests from untrimmed video , is an important step for video analysis . For state-of-the-art temporal action proposal methods , average pooling is often used to aggregate features in deep neural networks , which inevitably ignores the significances of different video clips . This notebook paper presents our system in the ActivityNet Dense Captioning in Video task ( task 3 ) . Temporal proposal generation and caption generation are both important to the dense captioning task . Therefore , we propose a proposal ranking model to employ a set of effective feature representations for proposal generation , and ensemble a series of caption models enhanced with context information to generate captions robustly on predicted proposals . Generating temporal action proposals remains a very challenging problem , where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence in long and untrimmed real-world videos . In this paper , we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator ( DBG ) , which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals . Our paper presents a new approach for temporal detection of human actions in long , untrimmed video sequences . We introduce Single-Stream Temporal Action Proposals ( SST ) , a new effective and efficient deep architecture for the generation of temporal action proposals . Our network can run continuously in a single stream over very long input video sequences , without the need to divide input into short overlapping clips or temporal windows for batch processing .
Our approach can classify objects with different weights ( motor-related features ) and different surface frictions ( haptics-related features ) with high accuracy ( over 0.8 ) . The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities ; thus , this may represent a new effective way to perform activity recognition .
The automated visual surveillance system will help to catch suspect in the scene , person identification in distance and re-identification , traffic management , sports , human computer interface , etc . Generally speaking , the human activity recognition in visual surveillance divided into following stages : object ( human or vehicle ) segmentation , feature extraction , object classification , object tracking , and activity recognition . Hence , the robust and object segmentation method in video camera is a very important phase because the rest of methods are strongly rely on it .
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition . Importantly , we extend the framework to incorporate hidden variables .
Thus , the abnormal behaviour can be detected . The activity detection and recognition is performed using an affordable RGB-D camera . 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 .
With the wide ranges of real-time event feed capturing devices , there has been significant progress in the area of digital image processing towards activity detection and recognition . Irrespective of the presence of various such devices , they are not adequate to meet dynamic monitoring demands of the visual surveillance system , and their features are highly limited towards complex human activity recognition system .
We present a detailed analysis of our approach against competing methods and explore variants such as hierarchy depth and choice of non-linear feature function . Obtained results are comparable to state-of-the-art methods on three important activity recognition benchmarks with classification performance of 76.7 % mAP on Hollywood2 , 69.4 % on HMDB51 , and 93.6 % on UCF101 .
In this paper we introduce a novel approach to classifier combination , which we term Weighted Ensemble Boosting . We apply the proposed algorithm to the problem of activity recognition in video , and compare its performance to different classifier combination methods . These include Approximate Bayesian Combination , Boosting , Feature Stacking , and the more traditional Sum and Product rules .
We then show the generalizability of our neurobiologically-inspired integration framework by casting the GAI within this infrastructure . Since Motion and image analysis are both important for activity recognition in video , we also develop a new approach that extends the Hamiltonian Monte Carlo ( HMC ) to allow us to simultaneously search over the combined motion and image space in a concerted manner using well-known Markov Chain Monte Carlo ( MCMC ) techniques . For motion analysis in video , we use tracks generated from the video to calculate the Hamiltonian equations of motion for the systems under study , thus utilizing analytical Hamiltonian dynamics to derive a physically significant HMC algorithm which can be used for activity analysis .
There is an increasing interest in activity recognition analysis due to the tremendous growth of sensors and devices that have recently brought significant attention to smart homes research which promotes inhabitants ’ comfort , security , and safety . Therefore , there is a necessity to have a comprehensive activity recognition approach which is not only limited to recognise human activities and to detect unknown/abnormal behaviors , but also could update a stream learning model in a real-time setting with sequential sensor data .
We propose a method based on an augmented version of canonical correlation analysis ( AuCCA ) for linking high-dimensional features to activity-specific spatial displacements over time . We compare this continuous and discriminative approach to other well established methods in the field of activity recognition and detection . In particular , we first improve activity detections by incorporating temporal forward and backward mappings for regularization of detections .
This paper presents a multisensor fusion framework for video activities recognition based on statistical reasoning and D-S evidence theory . Precisely , the framework consists in the combination of the events ’ uncertainty computation with the trained database and the fusion method based on the conflict management of evidences .
Tracking is a fundamental task in any video application requiring some degree of reasoning about objects of interest , as it allows to establish object correspondances between frames [ reference ] . It finds use in a wide range of scenarios such as automatic surveillance , vehicle navigation , video labelling , human - computer interaction and activity recognition . Given the location of an arbitrary target of interest in the first frame of a video , the aim of visual object tracking is to estimate its position in all the subsequent frames [ reference ][ reference ][ reference ] .
The task of estimating 3D human pose from 2D representations like monocular images or videos is an open research problem among the computer vision and graphics community for a long time . An understanding of human posture and limb articulation is important for high level computer vision tasks such as human action or activity recognition , sports analysis , augmented and virtual reality . A 2D representation of human pose , which is considered to be much easier to estimate , can be used for these tasks .
This paper presents a novel Hierarchical Self-Attention Network ( HISAN ) to generate spatial-temporal tubes for action localization in videos . The essence of HISAN is to combine the two-stream convolutional neural network ( CNN ) with hierarchical bidirectional self-attention mechanism , which comprises of two levels of bidirectional self-attention to efficaciously capture both of the long-term temporal dependency information and spatial context information to render more precise action localization .
The major drawback of such an approach is that the dictionary is learned in an unsupervised manner without considering the task ( classification or detection ) that follows it . In this work we develop task dependent(supervised ) dictionaries for action recognition and localization , i.e. , dictionaries that are best suited for the subsequent task . In the first part of the work , we propose a supervised max-margin framework for linear and non-linear Non-Negative Matrix Factorization ( NMF ) .
Hence , to make the machine learning models easily deployable and application ready , it is highly desirable to curtail the dependence of learning algorithms on engineered features so that the construction of novel algorithms can be much faster . This thesis proposes a novel approach to represent a video as a graph for action recognition and localization using only class label information . In addition to that , this thesis also proposes a novel subspace attention mechanism to learn to capture long-range inter-dependencies in visual data .
The ability to analyze the actions which occur in a video is essential for automatic understanding of sports . Action localization and recognition in videos are two main research topics in this context . In this chapter , we provide a detailed study of the prominent methods devised for these two tasks which yield superior results for sports videos .
An action pose from a test frame can be segmented by GrabCut algorithm using the initial segmentation mask from the matched leaf node as the user labeling . Our approach does not rely on background subtraction , and enables action localization and recognition in realistic and challenging conditions ( such as crowded backgrounds ) . Experimental results show that our approach achieves start-of-art performances on the Weizmann dataset , CMU action dataset and UCF sports action dataset .
We present a method for utilizing weakly supervised data for action localization in videos . We focus on sports video analysis , where videos contain scenes of multiple people .
Difficulties are encountered in the application of existing methods when modeling temporal structures of videos . In the present study , we developed a novel method , referred to as Gemini Network , for effective modeling of temporal structures and achieving high-performance temporal action localization . The significant improvements afforded by the proposed method are attributable to three major factors .
The loss of auxiliary classifiers increases the gradient signal that gets propagated back , and boosts our framework ’s performance . We achieve state-of-the-art performance for temporal action localization on two challenging temporal action localization datasets , THUMOS14 and ActivityNet .
This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018 : temporal action proposals , temporal action localization , dense-captioning events in videos , trimmed action recognition , and spatio-temporal action localization .
Our model is trained to simultaneously recognize and localize action classes in time and is based on two layer gated recurrent units ( GRU ) applied separately to two streams , i.e. appearance and optical flow streams . When used together with state-of-the-art person detection and tracking , our model is shown to improve substantially spatio-temporal action localization in videos . The gain is shown to be mainly due to improved temporal localization .
To provide further details about the existing action recognition methods in this area , we decompose the action recognition framework into three main steps of feature extraction , dictionary learning to represent a video , and classification ; we overview several successful techniques for each of these steps . We also overview the problem of spatio-temporal localization of actions and argue that , in general , it manifests a more challenging problem compared to action recognition . We study several recent methods for action localization which have shown promising results on sports videos .
We strive for spatio-temporal localization of actions in videos . The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations .
Current state-of-the-art approaches usually extract these information with separate networks and use an extra mechanism for fusion to get detections . In this work , we present YOWO , a unified CNN architecture for real-time spatiotemporal action localization in video streams . YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation .
For the integration , we introduce a Markov chain model which adds cues successively . The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization . The two contributions clearly improve the performance over respective baselines .
This model seeks to fuse richer information of global video clip , short human attention and long-term human activity into a unified model . We have participated in two tasks : Task A , the Kinetics challenge and Task B , spatio-temporal action localization challenge . For Kinetics , we achieve 21.59 % error rate .
The goal of this work is spatio-temporal action localization in videos , using only the supervision from video-level class labels . The state-of-the-art casts this weakly-supervised action localization regime as a Multiple Instance Learning problem , where instances are a priori computed spatio-temporal proposals .
We propose TAL-Net , an improved approach to temporal action localization in video that is inspired by the Faster RCNN object detection framework . TAL-Net addresses three key shortcomings of existing approaches : ( 1 ) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations ; ( 2 ) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields ; and ( 3 ) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important .
Temporal action proposal generation is an important yet challenging problem , since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content . This problem requires methods not only generating proposals with precise temporal boundaries , but also retrieving proposals to cover truth action instances with high recall and high overlap using relatively fewer proposals .
Temporal action recognition always depends on temporal action proposal generation to hypothesize actions . Applications require temporal action proposal generation to handle both large video dataset and generate more potential actions and suffer from high computation cost due to the bottleneck of temporal action proposal generation .
Temporal action proposal , which extracts segments of interests from untrimmed video , is an important step for video analysis . For state-of-the-art temporal action proposal methods , average pooling is often used to aggregate features in deep neural networks , which inevitably ignores the significances of different video clips .
This notebook paper presents our system in the ActivityNet Dense Captioning in Video task ( task 3 ) . Temporal proposal generation and caption generation are both important to the dense captioning task . Therefore , we propose a proposal ranking model to employ a set of effective feature representations for proposal generation , and ensemble a series of caption models enhanced with context information to generate captions robustly on predicted proposals .
Generating temporal action proposals remains a very challenging problem , where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence in long and untrimmed real-world videos . In this paper , we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator ( DBG ) , which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals .
Our paper presents a new approach for temporal detection of human actions in long , untrimmed video sequences . We introduce Single-Stream Temporal Action Proposals ( SST ) , a new effective and efficient deep architecture for the generation of temporal action proposals . Our network can run continuously in a single stream over very long input video sequences , without the need to divide input into short overlapping clips or temporal windows for batch processing .
Classification of grasping tasks based on EEG-EMG coherence Exploring object segmentation methods in visual surveillance for human activity recognition Boosted Markov Networks for Activity Recognition HMM-based Activity Recognition with a Ceiling RGB-D Camera A simplified machine learning approach for recognizing human activity Discriminatively Learned Hierarchical Rank Pooling Networks Weighted ensemble boosting for robust activity recognition in video A Physics-Based, Neurobiologically-Inspired Stochastic Framework for Activity Recognition Human Activity Recognition in Smart Homes Learn to Move: Activity Specific Motion Models for Tracking by Detection FUSION FRAMEWORK FOR VIDEO EVENT RECOGNITION Fast Online Object Tracking and Segmentation: A Unifying Approach Exploiting Temporal Information for 3D Human Pose Estimation Hierarchical Self-Attention Network for Action Localization in Videos Supervised dictionary learning for action recognition and localization Learning Representations for Image and Video Understanding Action Recognition in Realistic Sports Videos A unified tree-based framework for joint action localization, recognition and segmentation Attending to Distinctive Moments: Weakly-Supervised Attention Models for Action Localization in Video Rethinking Temporal Structure Modeling Method for Temporal Action Localization Gemini Network for Temporal Action Localization YH Technologies at ActivityNet Challenge 2018 Modeling Spatio-Temporal Human Track Structure for Action Localization Action Recognition in Realistic Sports Videos Spot On: Action Localization from Pointly-Supervised Proposals You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection Three Branches: Detecting Actions With Richer Features Spatio-Temporal Instance Learning: Action Tubes from Class Supervision Rethinking the Faster R-CNN Architecture for Temporal Action Localization BSN: Boundary Sensitive Network for Temporal Action Proposal Generation A high performance computing method for accelerating temporal action proposal generation Temporal Attention Network for Action Proposal RUC+CMU: System Report for Dense Captioning Events in Videos Fast Learning of Temporal Action Proposal via Dense Boundary Generator SST: Single-Stream Temporal Action Proposals
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ICPRAM ECCV Workshops (3) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ICCV ICCV Workshops ICIP PRCV (2) ECCV (5) ICCV ICIP CVPR
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