Spatio-Temporal Pyramid Model based on depth maps for action recognition
Haining Xu, Enqing Chen, Chengwu Liang, Lin Qi, Ling Guan · 2015
This paper presents a novel human action recognition method by using depth maps. Each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Under each projection view, we divide the entire depth maps into several sub-actions. The absolute difference between two consecutive projected maps is accumulated through a depth video (several sub-actions) sequence to form a Depth Motion Map (DMM) to describe the dynamic feature of an action. Also the difference within the threshold between two consecutive projected maps is calculated through the entire depth video to form another kind of Depth Static Map (DSM) to describe the static feature. Collectively, we call them Temporal Pyramid of Depth Model (TPDM). Then Spatial Pyramid Histograms of Oriented Gradient (SPHOG) is computed from the TPDM for the representation of an action. For classification, we apply support vector machine (SVM) to classify the proposed descriptorsbased on MSR Action3D dataset. Experimental results demonstrates the effectiveness of our proposed method.