The problem of human activity recognition via visual stimuli can be approached using manifold learning , since the silhouette ( binary ) images of a person undergoing a smooth motion can be represented as a manifold in the image space . While manifold learning methods allow the characterization of the activity manifolds , performing activity recognition requires distinguishing between manifolds . This invariably involves the extrapolation of learned activity manifolds to new silhouettes -a task that is not fully addressed in the literature . Activity recognition has been an active research topic in computer vision . Recently , the most successful approaches use dense trajectories that extract a large number of trajectories and features on the trajectories into a codeword . Furthermore , we show that the efficient algorithm outperforms the NSGA-II algorithm in terms of quality of the result set and efficiency ( for this optimisation problem ) . Finally we show that the algorithm returns hybrid classifiers that reflect the expert 's knowledge on activity recognition problem well . As one of many uses of body-worn inertial sensors , health monitoring applications can have a significant impact on the quality of life for a user even with inexpensive consumer electronics . In this paper , we address fluid intake monitoring as an activity recognition problem and conduct a user-study with 41 participants . We show that while an approach with a wrist-mounted sensor outperforms an approach with a head-mounted sensor , they can both be considered viable options for such a system . Signal segmentation is a crucial stage in the activity recognition process ; however , this has been rarely and vaguely characterized so far . Windowing approaches are normally used for segmentation , but no clear consensus exists on which window size should be preferably employed . Abstract Sensor-based activity recognition seeks to provide higher-level knowledge about human activities from multiple sensors such as accelerometer and gyroscope . Thanks to growing ubiquity of sensor-rich smartphones and wearable devices , activity recognition research has made tremendous progress in recent years . Many sensors , such as motion sensors and cameras , have been previously used for this task . Without a set of well-defined problems we can not start with a clear problem definition and ( equally important ) can not evaluate the state-of-the-art . In current activity recognition research it is often not clear how a particular approach might perform on the chosen problem as respective papers often do not formulate the problem definition clearly enough . This in turn is essential to develop a problem solution that is typically synthesized from previous research and often contains novel aspects . A system for managing , annotating and editing video sequences is a necessary tool in research on recognition of human actions and tracking people or objects . In addition annotation process is complex and expensive , so some people try to use crowdsourced marketplace based tools to make this process cost effective . Recognition of human actions by using wearable sensors has become an important research field . Segmentation to sensor data is a vital issue in reconstructing and understanding human daily actions , and strongly affects the accuracy of human actions recognition . The majority of methods for recognizing human actions are based on single-view video or multi-camera data . In this paper , we propose a novel multi-surface video analysis strategy . Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance , natural interfaces and rehabilitation . However , the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose , clothing and appearance . A primary phase now undertaking this issue is to achieve automatic recognition of human actions that are typical . In order to make human-computer interfaces genuinely likely , we need to develop a technology that tracks human effort , body behavior and interpret these movements in an effective way . We address recognition and localization of human actions in realistic scenarios . In contrast to the previous work studying human actions in controlled settings , here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance , motion , surrounding scenes , viewing angles and spatio-temporal extents . This work addresses the issue of image classification and localization of human actions based on visual data acquired from RGB sensors . Our approach is inspired by the success of deep learning in image classification . Human action recognition refers to automatic recognizing human actions from a video clip . In reality , there often exist multiple human actions in a video stream . Recognition of the human actions by computer vision has become an active research area in recent years . Due to the speed and the high similarity of the actions , the current algorithms can not get high recognition rate . Many applications depend on extracting human feature points from 2D images . e.g. noncontact anthropometric measurements , initializing 3D human body shape and recognizing human actions recognition . This paper represented a comparison between various edge detection techniques , which applied on an automated human feature points extraction algorithm from front and side images . The advantages of the this approach are : 1 ) it allows for using non-Euclidean similarity functions on the Positive Definite matrix manifold , which capture better the underlying geometry than directly comparing the sequences or their Hankel matrices , and 2 ) Gram matrices inherit desirable properties from the underlying Hankel matrices : their rank measure the complexity of the underlying dynamics , and the order and coefficients of the associated regressive models are invariant to affine transformations and varying initial conditions . The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences . In spite of its simplicity , the performance of this approach is competitive or better than using state-of-art approaches for this problem . It remains a challenge to extract spatial-temporal information from skeleton sequences for 3D human action recognition . Although most recent action recognition methods based on Recurrent Neural Networks ( RNN ) have achieved outstanding performance , one of the shortcomings of these methods is the tendency to overemphasize the temporal information . Due to the availability of large-scale skeleton datasets , 3D human action recognition has recently called the attention of computer vision community . Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints , in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix . For this purpose , we combine a deep learning network with geometric features extracted from data lie on a non-Euclidean space , which have been recently shown to be very effective to capture the geometric structure of the human pose . In particular , our approach claims to incorporate the intrinsic nature of the data characterized by Lie Group into deep neural networks and to learn more adequate geometric features for 3D action recognition problem . First , geometric features are extracted from 3D joints of skeleton sequences using the Lie group representation . This work proposes the volume integral ( VI ) as a new descriptor for three-dimensional action recognition . The descriptor transforms the actor ’s volumetric information into a two-dimensional representation by projecting the voxel data to a set of planes that maximize the discrimination of actions . Furthermore , this survey introduces a categorization of the most recent works in 3D skeleton-based action classification according to the adopted feature representation . This paper aims at being a starting point for practitioners who wish to approach the study of 3D action classification and gather insights on the main challenges to solve in this emerging field . HighlightsState of the art 3D skeleton-based action classification methods are reviewed . This dataset contains 120 different action classes including daily , mutual , and health-related activities . We evaluate the performance of a series of existing 3D activity analysis methods on this dataset , and show the advantage of applying deep learning methods for 3D-based human action recognition . Furthermore , we investigate a novel one-shot 3D activity recognition problem on our dataset , and a simple yet effective Action-Part Semantic Relevance-aware ( APSR ) framework is proposed for this task , which yields promising results for recognition of the novel action classes . 3D human action representation and recognition are important issues in many multimedia applications . While latent state approaches have been widely used for action modeling , previous works assume the latent states of actions are single attribute . RANs methodology is illustrated using a toydata problem , and evaluated for metrics Precision , Recall , Fl-score , and Accuracy , along with ROC curve analysis . RANs comparison is presented using a benchmark data from human activity recognition domain , yielding Precision= 98.20 % ( ca . ) , Recall= 97.62 % ( ca . ) , Fl-score= 97.8 ( ca . ) , and Accuracy= 97.62 % ( ca . ) . Sleep Detection data from SOCIALITE project is used to model active and inactive students , showing a convincing performance . The selected 25-dimension feature vector nearly contains all the information and after many times of test , it can achieve very high percentage of accuracy . Moreover , the method enables the learning velocity to outperform the state-of-the-art on the Human Activity Recognition domain . Experimental results verify that CMFGLR significantly outperforms several state-of-the-art methods . We apply collective matrix factorization to cross-domain action recognition . We build a novel graph Laplacian regularization term . In this paper , we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer . The proposed framework , referred to as Stratified Transfer Learning ( STL ) , can dramatically improve the classification accuracy for cross-domain activity recognition . Specifically , STL first obtains pseudo labels for the target domain via majority voting technique . Given sufficient cross-domain corresponding instances , one can learn a robust transformation that maps the features across the domains by using any multi-output regression task . However , this cross-domain corresponding data is not available for real-world transfer tasks across heterogeneous feature spaces such as , cross-domain activity recognition and cross-lingual text/sentiment classification . In this paper , we present a shared label space driven algorithm that transfers labeled knowledge between heterogeneous feature spaces . Performance of a Propagation Delay Tolerant ALOHA Protocol for Underwater Wireless Networks.- Performance of a Propagation Delay Tolerant ALOHA Protocol for Underwater Wireless Networks.- Time Synchronization in Heterogeneous Sensor Networks.- Stochastic Counting in Sensor Networks , or : Noise Is Good.- On the Deterministic Tracking of Moving Objects with a Binary Sensor Network.- An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks.- LiveNet : Using Passive Monitoring to Reconstruct Sensor Network Dynamics.- Broadcast Authentication in Sensor Networks Using Compressed Bloom Filters.- On the Urban Connectivity of Vehicular Sensor Networks.- FIT : A Flexible , LIght-Weight , and Real-Time Scheduling System for Wireless Sensor Platforms.- Automatic Collection of Fuel Prices from a Network of Mobile Cameras.- Techniques for Improving Opportunistic Sensor Networking Performance.- On the Average Case Communication Complexity for Detection in Sensor Networks.- Fault-Tolerant Compression Algorithms for Delay-Sensitive Sensor Networks with Unreliable Links.- Improved Distributed Simulation of Sensor Networks Based on Sensor Node Sleep Time.- Frugal Sensor Assignment.- Tug-of-War : An Adaptive and Cost-Optimal Data Storage and Query Mechanism in Wireless Sensor Networks.- Towards Diagnostic Simulation in Sensor Networks.- Sensor Placement for 3-Coverage with Minimum Separation Requirements.- Power Assignment Problems in Wireless Communication : Covering Points by Disks , Reaching few Receivers Quickly , and Energy-Efficient Travelling Salesman Tours.- Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks.- CaliBree : Since it is time-consuming and expensive to acquire abundant labeled data , transfer learning becomes necessary for HAR by transferring knowledge from existing domains . However , there are two challenges existing in cross-dataset activity recognition . The first challenge is source domain selection . Such as they need expensive computational cost and they are not suitable for modeling nonperiodic activity . In this paper , we propose a novel algorithm for three dimensional human activity recognition that combines spatial-domain local texture features and spatio-temporal local texture features . On the one hand , in order to extract spatial local texture features , we mix the RGB and depth image sequence which have been applied with ViBe ( Visual Background extractor ) and binarization operator . We present WiCAR , a WiFi-based in-car activity recognition framework that is able to remove domain-specific information in the received signals while retaining the activity related information to the maximum extent . A deep learning architecture integrated with domain adversarial training is applied to domain independent activity recognition . Specifically , we leverage multi-adversarial domain adaptation to avoid the discriminative structures mixing up for different domains . Evaluate the proposed algorithm on 2 well-defined problems and 5 real life data sets . In the multi-sensor activity recognition domain , the input space is often large and contains irrelevant and overlapped features . It is important to perform feature selection in order to select the smallest number of features which can describe the outputs . Such endeavors have traditionally been performed manually through human observation , making it extremely labor and time-intensive . Meanwhile , the development of low-cost micro-electro-mechanical systems ( MEMS ) with rapidly evolving computing , networking , and storage capabilities , along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain . Even though previous studies have shown promising results for equipment activity identification at limited levels of detail , they have a fundamental limitation in their reliance on the equipment vibration . The strength of the proposed model lies in that RAM achieves robustness in terms of inter-activity detection consistency and time efficiency with little overhead . Robustness property offers a potential to reduce the need for re-training an expert system , which faces the problem of growing set of activity classes in the real time activity recognition domain . We compared RAM for a set of hand oriented activities , against 8 different configurations , where SVM and K-Nearest Neighbour ( KNN ) classifiers are fed with different predefined features .
The problem of human activity recognition via visual stimuli can be approached using manifold learning , since the silhouette ( binary ) images of a person undergoing a smooth motion can be represented as a manifold in the image space . While manifold learning methods allow the characterization of the activity manifolds , performing activity recognition requires distinguishing between manifolds . This invariably involves the extrapolation of learned activity manifolds to new silhouettes -a task that is not fully addressed in the literature .
Activity recognition has been an active research topic in computer vision . Recently , the most successful approaches use dense trajectories that extract a large number of trajectories and features on the trajectories into a codeword .
Furthermore , we show that the efficient algorithm outperforms the NSGA-II algorithm in terms of quality of the result set and efficiency ( for this optimisation problem ) . Finally we show that the algorithm returns hybrid classifiers that reflect the expert 's knowledge on activity recognition problem well .
As one of many uses of body-worn inertial sensors , health monitoring applications can have a significant impact on the quality of life for a user even with inexpensive consumer electronics . In this paper , we address fluid intake monitoring as an activity recognition problem and conduct a user-study with 41 participants . We show that while an approach with a wrist-mounted sensor outperforms an approach with a head-mounted sensor , they can both be considered viable options for such a system .
Signal segmentation is a crucial stage in the activity recognition process ; however , this has been rarely and vaguely characterized so far . Windowing approaches are normally used for segmentation , but no clear consensus exists on which window size should be preferably employed .
Abstract Sensor-based activity recognition seeks to provide higher-level knowledge about human activities from multiple sensors such as accelerometer and gyroscope . Thanks to growing ubiquity of sensor-rich smartphones and wearable devices , activity recognition research has made tremendous progress in recent years . Many sensors , such as motion sensors and cameras , have been previously used for this task .
Without a set of well-defined problems we can not start with a clear problem definition and ( equally important ) can not evaluate the state-of-the-art . In current activity recognition research it is often not clear how a particular approach might perform on the chosen problem as respective papers often do not formulate the problem definition clearly enough . This in turn is essential to develop a problem solution that is typically synthesized from previous research and often contains novel aspects .
A system for managing , annotating and editing video sequences is a necessary tool in research on recognition of human actions and tracking people or objects . In addition annotation process is complex and expensive , so some people try to use crowdsourced marketplace based tools to make this process cost effective .
Recognition of human actions by using wearable sensors has become an important research field . Segmentation to sensor data is a vital issue in reconstructing and understanding human daily actions , and strongly affects the accuracy of human actions recognition .
The majority of methods for recognizing human actions are based on single-view video or multi-camera data . In this paper , we propose a novel multi-surface video analysis strategy .
Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance , natural interfaces and rehabilitation . However , the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose , clothing and appearance .
A primary phase now undertaking this issue is to achieve automatic recognition of human actions that are typical . In order to make human-computer interfaces genuinely likely , we need to develop a technology that tracks human effort , body behavior and interpret these movements in an effective way .
We address recognition and localization of human actions in realistic scenarios . In contrast to the previous work studying human actions in controlled settings , here we train and test algorithms on real movies with substantial variation of actions in terms of subject appearance , motion , surrounding scenes , viewing angles and spatio-temporal extents .
This work addresses the issue of image classification and localization of human actions based on visual data acquired from RGB sensors . Our approach is inspired by the success of deep learning in image classification .
Human action recognition refers to automatic recognizing human actions from a video clip . In reality , there often exist multiple human actions in a video stream .
Recognition of the human actions by computer vision has become an active research area in recent years . Due to the speed and the high similarity of the actions , the current algorithms can not get high recognition rate .
Many applications depend on extracting human feature points from 2D images . e.g. noncontact anthropometric measurements , initializing 3D human body shape and recognizing human actions recognition . This paper represented a comparison between various edge detection techniques , which applied on an automated human feature points extraction algorithm from front and side images .
The advantages of the this approach are : 1 ) it allows for using non-Euclidean similarity functions on the Positive Definite matrix manifold , which capture better the underlying geometry than directly comparing the sequences or their Hankel matrices , and 2 ) Gram matrices inherit desirable properties from the underlying Hankel matrices : their rank measure the complexity of the underlying dynamics , and the order and coefficients of the associated regressive models are invariant to affine transformations and varying initial conditions . The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences . In spite of its simplicity , the performance of this approach is competitive or better than using state-of-art approaches for this problem .
It remains a challenge to extract spatial-temporal information from skeleton sequences for 3D human action recognition . Although most recent action recognition methods based on Recurrent Neural Networks ( RNN ) have achieved outstanding performance , one of the shortcomings of these methods is the tendency to overemphasize the temporal information .
Due to the availability of large-scale skeleton datasets , 3D human action recognition has recently called the attention of computer vision community . Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints , in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix .
For this purpose , we combine a deep learning network with geometric features extracted from data lie on a non-Euclidean space , which have been recently shown to be very effective to capture the geometric structure of the human pose . In particular , our approach claims to incorporate the intrinsic nature of the data characterized by Lie Group into deep neural networks and to learn more adequate geometric features for 3D action recognition problem . First , geometric features are extracted from 3D joints of skeleton sequences using the Lie group representation .
This work proposes the volume integral ( VI ) as a new descriptor for three-dimensional action recognition . The descriptor transforms the actor ’s volumetric information into a two-dimensional representation by projecting the voxel data to a set of planes that maximize the discrimination of actions .
Furthermore , this survey introduces a categorization of the most recent works in 3D skeleton-based action classification according to the adopted feature representation . This paper aims at being a starting point for practitioners who wish to approach the study of 3D action classification and gather insights on the main challenges to solve in this emerging field . HighlightsState of the art 3D skeleton-based action classification methods are reviewed .
This dataset contains 120 different action classes including daily , mutual , and health-related activities . We evaluate the performance of a series of existing 3D activity analysis methods on this dataset , and show the advantage of applying deep learning methods for 3D-based human action recognition . Furthermore , we investigate a novel one-shot 3D activity recognition problem on our dataset , and a simple yet effective Action-Part Semantic Relevance-aware ( APSR ) framework is proposed for this task , which yields promising results for recognition of the novel action classes .
3D human action representation and recognition are important issues in many multimedia applications . While latent state approaches have been widely used for action modeling , previous works assume the latent states of actions are single attribute .
RANs methodology is illustrated using a toydata problem , and evaluated for metrics Precision , Recall , Fl-score , and Accuracy , along with ROC curve analysis . RANs comparison is presented using a benchmark data from human activity recognition domain , yielding Precision= 98.20 % ( ca . ) , Recall= 97.62 % ( ca . ) , Fl-score= 97.8 ( ca . ) , and Accuracy= 97.62 % ( ca . ) . Sleep Detection data from SOCIALITE project is used to model active and inactive students , showing a convincing performance .
The selected 25-dimension feature vector nearly contains all the information and after many times of test , it can achieve very high percentage of accuracy . Moreover , the method enables the learning velocity to outperform the state-of-the-art on the Human Activity Recognition domain .
Experimental results verify that CMFGLR significantly outperforms several state-of-the-art methods . We apply collective matrix factorization to cross-domain action recognition . We build a novel graph Laplacian regularization term .
In this paper , we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer . The proposed framework , referred to as Stratified Transfer Learning ( STL ) , can dramatically improve the classification accuracy for cross-domain activity recognition . Specifically , STL first obtains pseudo labels for the target domain via majority voting technique .
Given sufficient cross-domain corresponding instances , one can learn a robust transformation that maps the features across the domains by using any multi-output regression task . However , this cross-domain corresponding data is not available for real-world transfer tasks across heterogeneous feature spaces such as , cross-domain activity recognition and cross-lingual text/sentiment classification . In this paper , we present a shared label space driven algorithm that transfers labeled knowledge between heterogeneous feature spaces .
Performance of a Propagation Delay Tolerant ALOHA Protocol for Underwater Wireless Networks.- Performance of a Propagation Delay Tolerant ALOHA Protocol for Underwater Wireless Networks.- Time Synchronization in Heterogeneous Sensor Networks.- Stochastic Counting in Sensor Networks , or : Noise Is Good.- On the Deterministic Tracking of Moving Objects with a Binary Sensor Network.- An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks.- LiveNet : Using Passive Monitoring to Reconstruct Sensor Network Dynamics.- Broadcast Authentication in Sensor Networks Using Compressed Bloom Filters.- On the Urban Connectivity of Vehicular Sensor Networks.- FIT : A Flexible , LIght-Weight , and Real-Time Scheduling System for Wireless Sensor Platforms.- Automatic Collection of Fuel Prices from a Network of Mobile Cameras.- Techniques for Improving Opportunistic Sensor Networking Performance.- On the Average Case Communication Complexity for Detection in Sensor Networks.- Fault-Tolerant Compression Algorithms for Delay-Sensitive Sensor Networks with Unreliable Links.- Improved Distributed Simulation of Sensor Networks Based on Sensor Node Sleep Time.- Frugal Sensor Assignment.- Tug-of-War : An Adaptive and Cost-Optimal Data Storage and Query Mechanism in Wireless Sensor Networks.- Towards Diagnostic Simulation in Sensor Networks.- Sensor Placement for 3-Coverage with Minimum Separation Requirements.- Power Assignment Problems in Wireless Communication : Covering Points by Disks , Reaching few Receivers Quickly , and Energy-Efficient Travelling Salesman Tours.- Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks.- CaliBree :
Since it is time-consuming and expensive to acquire abundant labeled data , transfer learning becomes necessary for HAR by transferring knowledge from existing domains . However , there are two challenges existing in cross-dataset activity recognition . The first challenge is source domain selection .
Such as they need expensive computational cost and they are not suitable for modeling nonperiodic activity . In this paper , we propose a novel algorithm for three dimensional human activity recognition that combines spatial-domain local texture features and spatio-temporal local texture features . On the one hand , in order to extract spatial local texture features , we mix the RGB and depth image sequence which have been applied with ViBe ( Visual Background extractor ) and binarization operator .
We present WiCAR , a WiFi-based in-car activity recognition framework that is able to remove domain-specific information in the received signals while retaining the activity related information to the maximum extent . A deep learning architecture integrated with domain adversarial training is applied to domain independent activity recognition . Specifically , we leverage multi-adversarial domain adaptation to avoid the discriminative structures mixing up for different domains .
Evaluate the proposed algorithm on 2 well-defined problems and 5 real life data sets . In the multi-sensor activity recognition domain , the input space is often large and contains irrelevant and overlapped features . It is important to perform feature selection in order to select the smallest number of features which can describe the outputs .
Such endeavors have traditionally been performed manually through human observation , making it extremely labor and time-intensive . Meanwhile , the development of low-cost micro-electro-mechanical systems ( MEMS ) with rapidly evolving computing , networking , and storage capabilities , along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain . Even though previous studies have shown promising results for equipment activity identification at limited levels of detail , they have a fundamental limitation in their reliance on the equipment vibration .
The strength of the proposed model lies in that RAM achieves robustness in terms of inter-activity detection consistency and time efficiency with little overhead . Robustness property offers a potential to reduce the need for re-training an expert system , which faces the problem of growing set of activity classes in the real time activity recognition domain . We compared RAM for a set of hand oriented activities , against 8 different configurations , where SVM and K-Nearest Neighbour ( KNN ) classifiers are fed with different predefined features .
Extrapolating Learned Manifolds for Human Activity Recognition Evaluation of Vision-Based Human Activity Recognition in Dense Trajectory Framework Multi-objective learning of hybrid classifiers Fluid intake recognition using inertial sensors Window Size Impact in Human Activity Recognition ActiPPG: Using deep neural networks for activity recognition from wrist-worn photoplethysmography (PPG) sensors Standing on the Shoulders of Other Researchers - A Position Statement Video Editor for Annotating Human Actions and Object Trajectories Online data segmentation based on clustering algorithm and autoregressive model for human actions recognition Multi-surface analysis for human action recognition in video 3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold Human gestures and recognizing faces in natural expressions by considering computer vision techniques on mobile devices Retrieving actions in movies Mind the Regularized GAP, for Human Action Classification and Semi-supervised Localization based on Visual Saliency Multi-Label Zero-Shot Human Action Recognition via Joint Latent Embedding Combining Multi-scale Directed Depth Motion Maps and Log-Gabor Filters for Human Action Recognition COMPARISON OF EDGE DETECTION TECHNIQUES FOR HUMAN FEATURE POINTS EXTRACTION Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold Skeleton-Based Human Action Recognition Using Spatial Temporal 3D Convolutional Neural Networks SkeleMotion: A New Representation of Skeleton Joint Sequences based on Motion Information for 3D Action Recognition Action Recognition from 3D Skeleton Sequences using Deep Networks on Lie Group Features Three-dimensional action recognition using volume integrals 3D skeleton-based human action classification: A survey NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding Learning Composite Latent Structures for 3D Human Action Representation and Recognition Perceiving Abstract Concepts Via Evolving Computational Cognitive Modeling HL-HAR: Hierarchical Learning Based Human Activity Recognition in Wearable Computing Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization Stratified Transfer Learning for Cross-domain Activity Recognition Supervised heterogeneous transfer learning using random forests Distributed Computing in Sensor Systems, 4th IEEE International Conference, DCOSS 2008, Santorini Island, Greece, June 11-14, 2008, Proceedings Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer Learning 3D human behavior recognition based on spatiotemporal texture features WiCAR: WiFi-based in-Car Activity Recognition with Multi-Adversarial Domain Adaptation Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members RAM: Real Time Activity Monitoring with feature extractive training
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ICIP (1) ISVC (1) ECAI iWOAR ACIIDS (2) ICCV VISIGRAPP (4: VISAPP) CVPR ICME ICPR IJCNN ICCCS (2) COMAD/CODS HSI IWQoS
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