Action recognition based on feature-level fusion

Wanli Cheng; Enqing Chen

doi:10.1117/12.2502864

9 August 2018 Action recognition based on feature-level fusion

Wanli Cheng, Enqing Chen

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060F (2018) https://doi.org/10.1117/12.2502864
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Wanli Cheng and Enqing Chen "Action recognition based on feature-level fusion", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060F (9 August 2018); https://doi.org/10.1117/12.2502864

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KEYWORDS

RGB color model

3D modeling

Video

Matrices

Principal component analysis

Video surveillance

Motion models

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