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Boosting EigenActions: A new algorithm for human action categorization

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2 Author(s)
Chang Liu ; Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong ; Yuen, P.C.

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.

Published in:

Automatic Face & Gesture Recognition, 2008. FG '08. 8th IEEE International Conference on

Date of Conference:

17-19 Sept. 2008

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