Boosting EigenActions: A new algorithm for human action categorization

This paper proposes a boosting EigenActions algorithm for human action categorization. In determining the EigenActions, a spatio-temporal information saliency is first calculated from the video sequence by estimating pixel density function. Since human action can be approximated as a periodic motion, salient action unit, which is one cycle of the motion, is extracted and EigenActions are determined using principle component analysis. A human action classifier is developed by multi-class Adaboost algorithm. Weizmann human action database with ninety different human actions is used to evaluate our proposed algorithm. The recognition accuracy is 98.3%. A comparison with two latest methods on human action recognition is also reported.