Human Behaviour Analysis based on Sparse Coding

摘要

Sparse coding and compressive sensing have attracted lots of interest in the computer vision area. This paper proposes a new scheme to recognize human motions in video sequences based on the sparse representation of image frames. Each frame of a video is transformed to a linear combination of a few elements in a dictionary. The class label of the video is determined based on the reconstruction errors of individual frames or the overall reconstruction error of the video. A series of experiments were conducted to evaluate the performance of the proposed method. Experimental results demonstrate that the sparse representation method achieves accuracy on par with or exceeding that of existing methods.