Behavior recognition based on sit posture using principle component analysis
基于PCA的坐姿行为识别*

According to the usual sit posture, this paper recognized 8 kinds behavior of human based on sit posture using PCA. Firstly, detected the motion area by background contrast attenuation method. Then, considering the clustered skin area in a fixed region of YCbCr space which had ellipse-like projection in CbCr plane, extracted the skin area of motion object. Finally, realized the posture recognition by PCA on the grayscale image of skin. The experimental results show that the average accuracy of behavior recognizing is 84.92% and the proposed algorithm is reasonably robust in shadow and varying luminance.