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Proceedings Paper

High-speed railway clearance surveillance system based on convolutional neural networks
Author(s): Yang Wang; Zujun Yu; Liqiang Zhu; Baoqing Guo
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Paper Abstract

In this paper, the convolutional neural networks with the pre-trained kernels are applied to the video surveillance system, which has been built along the Shanghai-Hangzhou high-speed railway to monitor the railway clearance scene and will output the alarm images with the dangerous intruding objects in. The video surveillance system will firstly generate the images which are suspected of containing the dangerous objects intruding the clearance. The convolutional neural networks with the pre-trained kernels are applied to process these suspicious images to eliminating the false alarm images, only contain the trains and the empty clearance scene, from other suspicious images before the final output. Experimental result shows that, the process of each test image only takes 0.16 second and has a high accuracy.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100335S (29 August 2016); doi: 10.1117/12.2245128
Show Author Affiliations
Yang Wang, Beijing Jiaotong Univ. (China)
Zujun Yu, Beijing Jiaotong Univ. (China)
Liqiang Zhu, Beijing Jiaotong Univ. (China)
Baoqing Guo, Beijing Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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