The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Recognizing and Tracking Human Action
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Robust Real-Time Face Detection
International Journal of Computer Vision
View-Invariant Human Activity Recognition Based on Shape and Motion Features
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Action recognition for surveillance applications using optic flow and SVM
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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This paper studies the technique of human action recognition using spatio-temporal features. We concentrate on the motion and the shape patterns produced by different actions for action recognition. The motion patterns generated by the actions are captured by the optical flows. The Shape information is obtained by Viola-Jones features. Spatial features comprises of motion and shape information from a single frame. Spatio-temporal descriptor patterns are formed to improve the accuracy over spatial features. Adaboost learns and classifies the descriptor patterns. We report the accuracy of our system on a standard Weizmann dataset.