Appearance-based representation and recognition of human motions

This paper employs an appearance-based eigenspace technique for representing and recognizing various human motions in terms of their postures. Various human motions are captured by video camera and they are sampled into defined image frames. These sequential images create a multi-dimensional eigenspace in which the motions are represented based on their postures change and this eigenspace is used for recognizing unknown postures/motions based on a MDL (minimum description length) principle. Since human clothes have an influence on creating an eigenspace, we employ blurred edge images throughput learning and training stages instead of original gray-scale images. The proposed method provides eigenspace updating when a new observation becomes available. Experimental results show satisfactory performance on representing and recognizing various motions and/or postures in the proposed approach.