Human action recognition based on 3D body mask and depth spatial-temporal maps

Abstract

In this paper, a method based on depth spatial-temporal maps(DSTMs) is presented for human action recognition from depth video sequences, which provides compact global spatial and temporal information of human motion for action recognition. In our approach, the initial frame of depth sequences is dilated to generate 3D body mask. The new depth sequences of major part of the human body are then computed after using 3D body mask on each depth frame. We project each frame of the new depth sequences onto three orthogonal axes to get three binary lists. Under each projection axis, binary lists are stitching in order through an entire depth sequence forming a DSTM. We evaluate our method on two standard databases. Experimental results show that this method could effectively capture the spatial and temporal information of human motion and improve the accuracy of human action recognition.