Action recognition based on multiple key motion history images

Human action recognition in video sequences is an important research topic in computer vision, and motion history image (MHI) is widely taken for recognition due to its simplicity. However, it may be not robust to describe an action by only a single MHI. Therefore, an action recognition scheme by using multiple key MHIs (MKMHIs) is proposed. Firstly, an adaptive method for key MHIs selection is proposed based on entropy of MHIs. Then a new combined feature vector of MHIs by spatial pyramid matching (SPM) is defined to describe spatiotemporal characteristics of actions. Here, the proposed solution is composed of SPM two dimensional entropy (2D-entropy) of MHI and SPM Zernike moment of motion history image edge (MHIE), and our combined feature is lower compared with Local Binary Pattern Histogram(LBP_H). At last, action recognition is performed by SVM and voting. Experimental results show the proposed method based on MKMHIs can improve the action recognition ratio.