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Sensors 2014, 14(6), 10578-10597; doi:10.3390/s140610578

Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques

Department of Electronic Engineering, National Chin-Yi University of Technology 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
College of Electric and Control Engineering, Xi'an University of Science and Technology, 58 Yan-Ta Road, Xi'an City 710054, Shaanxi Province, China
Author to whom correspondence should be addressed.
Received: 14 March 2014 / Revised: 31 May 2014 / Accepted: 4 June 2014 / Published: 16 June 2014


Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas. View Full-Text
Keywords: railway crossing; object extraction; background subtraction; linear regression railway crossing; object extraction; background subtraction; linear regression

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Chen, W.-Y.; Wang, M.; Fu, Z.-X. Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques. Sensors 2014, 14, 10578-10597.

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