Paper
5 March 2018 Railway clearance intrusion detection method with binocular stereo vision
Xingfang Zhou, Baoqing Guo, Wei Wei
Author Affiliations +
Proceedings Volume 10710, Young Scientists Forum 2017; 1071012 (2018) https://doi.org/10.1117/12.2315784
Event: Young Scientists Forum 2017, 2017, Shanghai, China
Abstract
In the stage of railway construction and operation, objects intruding railway clearance greatly threaten the safety of railway operation. Real-time intrusion detection is of great importance. For the shortcomings of depth insensitive and shadow interference of single image method, an intrusion detection method with binocular stereo vision is proposed to reconstruct the 3D scene for locating the objects and judging clearance intrusion. The binocular cameras are calibrated with Zhang Zhengyou’s method. In order to improve the 3D reconstruction speed, a suspicious region is firstly determined by background difference method of a single camera’s image sequences. The image rectification, stereo matching and 3D reconstruction process are only executed when there is a suspicious region. A transformation matrix from Camera Coordinate System(CCS) to Track Coordinate System(TCS) is computed with gauge constant and used to transfer the 3D point clouds into the TCS, then the 3D point clouds are used to calculate the object position and intrusion in TCS. The experiments in railway scene show that the position precision is better than 10mm. It is an effective way for clearance intrusion detection and can satisfy the requirement of railway application.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xingfang Zhou, Baoqing Guo, and Wei Wei "Railway clearance intrusion detection method with binocular stereo vision", Proc. SPIE 10710, Young Scientists Forum 2017, 1071012 (5 March 2018); https://doi.org/10.1117/12.2315784
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KEYWORDS
Cameras

Calibration

3D image reconstruction

3D modeling

3D image processing

Computer intrusion detection

Clouds

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