Human Action Recognition Using 2-D Spatio-Temporal Templates

A framework for human action modeling and recognition in continuous action sequences is proposed. A star figure enclosed by a bounding convex polygon is used to effectively represent the extremities of the silhouette of a human body. Thus, human actions are recorded as a sequence of the star figure's parameters, which is then used for action modeling. To model human actions in a compact manner while characterizing their spatio-temporal patterns, star figure parameters are represented by a 2-D feature map, which is used and regarded as a spatio-temporal template. Experiments to evaluate the performance of the proposed framework show that it can recognize human actions in an efficient and effective manner.

[1]  Sheng-Wen Shih,et al.  Atomic Human Action Segmentation Using a Spatio-Temporal Probabilistic Framework , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[2]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[4]  Rita Cucchiara,et al.  Probabilistic posture classification for Human-behavior analysis , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Jake K. Aggarwal,et al.  Segmentation and recognition of continuous human activity , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[6]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[7]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, ICPR 2004.

[8]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Larry S. Davis,et al.  Ghost: a human body part labeling system using silhouettes , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[10]  Ferran Marqués,et al.  Silhouette-based probabilistic 2D human motion estimation for real-time applications , 2005, IEEE International Conference on Image Processing 2005.

[11]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[12]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[13]  Monique Thonnat,et al.  Human Posture Recognition in Video Sequence , 2003 .