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Proceedings Paper

Convolutional neural networks based on sparse coding for human postures recognition
Author(s): Ning Yang; Yawei Li; Yuliang Yang; Mengyu Zhu
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Paper Abstract

This paper presents a convolutional neural networks (CNN) based on sparse coding for human postures recognition. It’s an unsupervised approach for color multi-channel processing. The improvement of the method is mainly reflected in two aspects. We transform sample images into patches and make a decorrelation between input patches and reconstructed patches. In addition, we use the convolution kernels extracted by sparse coding to replace the initialization of the convolution kernels for human postures recognition. The proposed method is tested in the public KTH pedestrian behavior dataset and HUMAN-V2 self-test dataset. Compared with the traditional way, our approach shortens the training time a lot and also improves the recognition rate. Our experimental results verifies the effectiveness.

Paper Details

Date Published: 24 October 2017
PDF: 7 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104622B (24 October 2017); doi: 10.1117/12.2284449
Show Author Affiliations
Ning Yang, Univ. of Science and Technology Beijing (China)
Yawei Li, Univ. of Science and Technology Beijing (China)
Yuliang Yang, Univ. of Science and Technology Beijing (China)
Mengyu Zhu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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