Local mean spatio-temporal feature for depth image-based speed-up action recognition

With the promptly growing population of the low-cost Microsoft Kinect sensor, action recognition, which is a hard yet important problem in computer vision, has been received substantial attention. However, most existing approaches in action recognition spend much time on feature detection even though these methods can achieve high recognition rates. In this paper, we propose a local mean spatio-temporal feature (LMSF) to speed up depth image based action recognition. In particular, we solve the problem from three aspects: (1) associate the 4D normals by a local mean spatio-temporal neighborhood; (2) extract motion frames by detecting the differences between consecutive frames; (3) reduce redundant normals extracted from depth cloud points by sparse coding. The proposed approach is tested on two public benchmark datasets, i.e., MSRAction3D and MSRGesture3D. Experimental results demonstrate the advantages of our improvement method and the state-of-the-art performance on processing speed.