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

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

1,* , 1,2
 and
2
Received: 14 March 2014 / Revised: 31 May 2014 / Accepted: 4 June 2014 / Published: 16 June 2014

Abstract

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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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