Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Sparse B-spline polynomial descriptors for human activity recognition
Image and Vision Computing
Viewpoint manifolds for action recognition
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Rate-invariant recognition of humans and their activities
IEEE Transactions on Image Processing
Unsupervised human motion analysis using automatic label trees
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Applications of a simple characterization of human gait in surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Human activity analysis: A review
ACM Computing Surveys (CSUR)
2D action recognition serves 3D human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Improving the accuracy of action classification using view-dependent context information
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Dynamic events as mixtures of spatial and temporal features
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
International Journal of Computer Vision
A tree-based approach to integrated action localization, recognition and segmentation
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
View invariant action recognition using weighted fundamental ratios
Computer Vision and Image Understanding
Exploring discriminative pose sub-patterns for effective action classification
Proceedings of the 21st ACM international conference on Multimedia
Video event description in scene context
Neurocomputing
A unified tree-based framework for joint action localization, recognition and segmentation
Computer Vision and Image Understanding
| Hi-index | 0.00 |
One of the fundamental challenges of recognizing actions is accounting for the variability that arises when arbitrary cameras capture humans performing actions. In this paper, we explicitly identify three important sources of variability: (1) viewpoint, (2) execution rate, and (3) anthropometry of actors, and propose a model of human actions that allows us to investigate all three. Our hypothesis is that the variability associated with the execution of an action can be closely approximated by a linear combination of action bases in joint spatio-temporal space. We demonstrate that such a model bounds the rank of a matrix of image measurements and that this bound can be used to achieve recognition of actions based only on imaged data. A test employing principal angles between subspaces that is robust to statistical fluctuations in measurement data is presented to find the membership of an instance of an action. The algorithm is applied to recognize several actions, and promising results have been obtained.