Action Recognition Using Multi-Scale Histograms of Oriented Gradients based Depth Motion Trail Images

摘要

In this paper, we propose a novel approach based on Depth Maps and compute Multi-Scale Histograms of Oriented Gradient (MSHOG) from sequences of depth maps to recognize actions. Each depth frame in a depth video sequence is projected onto three orthogonal Cartesian planes. Under each projection view, the absolute difference between two consecutive projected maps is accumulated through a depth video sequence to form a Depth Map, which is called Depth Motion Trail Images (DMTI). The MSHOG is then computed from the Depth Maps for the representation of an action. In addition, we apply L2-Regulanzed Collaborative Representation (L2-CRC) to classify actions. We evaluate the proposed approach on MSR Action3D dataset and MSRGesture3D dataset. Promising experimental result demonstrates the effectiveness of our proposed method.