This paper presents an approach to recognize human activity based on principal component analysis (PCA). By a Gaussian model of skin color in HSI color space

our system tracks human face and hands incorporating with the constraints of motion and region continuity. A constant acceleration motion estimation and Schwarz representation based shape matching are applied to create correspondence between frames. Then motion parameters of faces and hands are represented and matched with the parameter curves of exemplars in PCA framework. Through modeling the spatio-temporal variants of each type of activity

recognition can be achieved although subject and imaging condition are different from those of exemplars. Examples of Taiji postures recognition are studied and discussed to illustrate our method. The experiment shows that this activity recognition approach is of low confusion rate and robust in some degree. We believe this approach can be applied to develop an activity interpretation system.Applications fields of this work include indexing video based on motion semantic description

assistant exercise training

video surveillance efficient video coding

etc.