Fast Online Action Recognition with Boosted Combinational Motion Features

In this paper, we propose a fast and robust online action recognition method. The main features of the proposed method are: 1) to select a small number of critical motion features from a very large set of motion feature templates and to release humans from task of designing critical motion features, 2) to require very small calculation cost for recognition compared to conventional methods, 3) to exploit "combinational motion features" which we propose as a new conception so as to construct a robust action recognizer. We evaluated the proposed method to gait action recognition, such as walking and running, by utilizing motion capture data. In the result, the proposed method reduced parameters given by human to action recognizer and lessened human\´s task. In addition, the proposed method needed very small calculation cost for recognition, and can recognize robustly as much as conventional action recognition method based on support vector machine. Moreover, the introduction of combinational motion features enhanced recognition performance