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
Matching actions in presence of camera motion
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Viewpoint invariant sign language recognition
Computer Vision and Image Understanding
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
View Invariant Human Action Recognition Based on Factorization and HMMs
IEICE - Transactions on Information and Systems
International Journal of Computer Vision
Rate-invariant recognition of humans and their activities
IEEE Transactions on Image Processing
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
Action and gait recognition from recovered 3-D human joints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
A survey of vision-based methods for action representation, segmentation and recognition
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
View invariant action recognition using weighted fundamental ratios
Computer Vision and Image Understanding
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In this paper, we propose a novel approach to matching human actions using semantic correspondence between human bodies with an eye towards invariant analysis of activity. The correspondences are used to provide geometric constraints between multiple anatomical landmarks (e.g. hands, shoulders and feet) to match actions performed from different viewpoints and in different environments. The fact that the human body has certain anthropometric proportion allows innovative use of the machinery of epipolar geometry to provide constraints to accurately analyze actions performed by different people leading to some interesting results. Temporally invariant matching is performed, using non-linear time warping, to ensure that similar actions performed at different rates are accurately matched as well. Thus, the proposed algorithm guarantees that both temporal and view invariance is maintained in matching. We demonstrate the versatility of our algorithm in a number of challenging sequences and applications.