关键词: 人体行为识别, 深度运动图, RGB, 密集轨迹, VGG-16

Abstract: A new human action recognition algorithm based on Depth Motion Maps（DMMs） and dense trajectory is proposed to fuse depth information of depth map sequences and rich texture information in RGB video sequences. It is not affected by environmental factors in depth map sequences easily including illumination. The Convolutional Neural Network（CNN） is utilized to train the DMM data and also extract the high-level features of the network as the static feature representation of the video. The dense trajectories are applied to describe the dynamic information of the RGB video sequences. Furthermore, the static and the dynamic features are connected to a series of the feature representation of the entire video which also is injected to the Support Vector Machine（SVM） for the video classification. The experimental results show that the algorithm can effectively extract depth information and texture information, achieve better recognition results on the public action recognition of library UTD-MHAD and MSR Daily Activity 3D.

Key words: human action recognition, depth motion maps, RGB, dense trajectory, VGG-16