The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Action Signature: A Novel Holistic Representation for Action Recognition
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Integrating local action elements for action analysis
Computer Vision and Image Understanding
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into elementary points from which a Decomposable Triangulated Graph will be built. The temporal variation of human activity is encoded in the velocity distribution of each node in the graph, while the graph structure shows the spatial configuration of all the nodes in the action. Tracking trajectories of unlabeled good feature points are correctly labeled using Maximum a Posterior probability. Dynamic Programming is then implemented to boost up the exhaustive search for the optimal labeling of unknown body parts and the best possible action. A simple and efficient technique for building the optimal structure of the human action graph is also implemented. Experimental results on the KTH dataset proves the success and potential applications of this proposed technique.