Action recognition using multi-scale histograms of oriented gradients based depth motion trail Images

Guanxi Wang; Yun Tie; Lin Qi

doi:10.1117/12.2281553

21 July 2017 Action recognition using multi-scale histograms of oriented gradients based depth motion trail Images

Guanxi Wang, Yun Tie, Lin Qi

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104200I (2017) https://doi.org/10.1117/12.2281553
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

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-Regularized 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.

Citation Download Citation

Guanxi Wang, Yun Tie, and Lin Qi "Action recognition using multi-scale histograms of oriented gradients based depth motion trail Images", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104200I (21 July 2017); https://doi.org/10.1117/12.2281553

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