Abstract: To solve the problem of low accuracy of human motion recognition in complex scenes, an improved human motion recognition system based on deformable convolution network (DCN) and deformable part model (DPM) is constructed. Firstly, the number of the DPM component filters are increased from 5 to 8, and the branch and bound method is combined to improve the accuracy by about 11% and the speed by about 3 times. Secondly, DCN is used to sample the points of interest according to the movements of human body. Then, the improved DPM and DCN are fused before deformable pool- ing. Finally, the input data is identified by the full connection layer.Experimental results show that this method can identify the results more quickly and accurately on the human movement dataset.

Key words: human motion recognition, deformable convolution, deformable interest pooling, deformable part model algorithm, convolutional neural network, branch and bound algorithm