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Proceedings Paper

Multi-view indoor human behavior recognition based on 3D skeleton
Author(s): Ling Peng; Tongwei Lu; Feng Min
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Paper Abstract

For the problems caused by viewpoint changes in activity recognition, a multi-view interior human behavior recognition method based on 3D framework is presented. First, Microsoft's Kinect device is used to obtain body motion video in the positive perspective, the oblique angle and the side perspective. Second, it extracts bone joints and get global human features and the local features of arms and legs at the same time to form 3D skeletal features set. Third, online dictionary learning on feature set is used to reduce the dimension of feature. Finally, linear support vector machine (LSVM) is used to obtain the results of behavior recognition. The experimental results show that this method has better recognition rate.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130Y (14 December 2015); doi: 10.1117/12.2205425
Show Author Affiliations
Ling Peng, Wuhan Institute of Technology (China)
Tongwei Lu, Wuhan Institute of Technology (China)
Feng Min, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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