Human Action Recognition Based on Spatio-temporal Features

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.