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14 December 2015 Multi-view indoor human behavior recognition based on 3D skeleton
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130Y (2015) https://doi.org/10.1117/12.2205425
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ling Peng, Tongwei Lu, and Feng Min "Multi-view indoor human behavior recognition based on 3D skeleton", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130Y (14 December 2015); https://doi.org/10.1117/12.2205425
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