21 July 2017 Action recognition using multi-scale histograms of oriented gradients based depth motion trail Images
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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.
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Guanxi Wang, Guanxi Wang, Yun Tie, Yun Tie, Lin Qi, 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); doi: 10.1117/12.2281553; https://doi.org/10.1117/12.2281553
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