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Proceedings Paper

Action recognition based on feature-level fusion
Author(s): Wanli Cheng; Enqing Chen
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Paper Abstract

In this paper, we propose a new effective and robust framework to recognize human actions from depth map sequence. Firstly, 3D motion trail model (3DMTM) is extracted to represent the temporal motion information. Then, two effective heterogeneous features are proposed to descried actions more comprehensive based on 3DMTM. By computing Multilayer Histograms of Oriented Gradient (MHOG) on 3DMTM, 3DMTM-MHOG is obtained to describe local detail information of different actions. Combining Gist and 3DMTM, we can get 3DMTM-Gist to model holistic structural feature of actions. The feature-level fusion method is utilized to merge two descriptors to form the final feature. Lastly, support vector machine (SVM) classification is used for multi-class action recognition. Experimental results on public depth action dataset (MSR Action3D dataset) show that our method is superior to the state-of-the-art methods.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060F (9 August 2018); doi: 10.1117/12.2502864
Show Author Affiliations
Wanli Cheng, Zhengzhou Univ. (China)
Enqing Chen, Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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