关键词: 深度图像, 投影, Gabor特征, 极限学习机, 动作识别

Abstract: Concerning the high time complexity and low recognition rate of action recognition algorithm in traditional RGB video, this paper proposes an action recognition approach based on depth image. Firstly, each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Secondly, the Gabor feature is extracted from the projected maps in three projective views. Lastly, the features are used to train extreme learning machine classifiers that are used to classify actions. Experiments on public dataset MSR Action 3D show the method achieves average recognition rate 97.80%, 99.10% and 88.35% respectively on different three group experiments. The elapsed time of recognizing single depth video is less than 1 second. The results show the proposed method can effectively identify the human motion in depth image sequence and satisfy the real-time requirement of the depth image sequence.

Key words: depth image, projection, Gabor feature, extreme learning machine, action recognition