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- Title
Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques.
- Authors
Wen-Yuan Chen; Mei Wang; Zhou-Xing Fu
- 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.
- Subjects
RAILROAD accidents; RAILROAD research; IMAGE processing; IMAGING systems; ACCIDENT prevention
- Publication
Sensors (14248220), 2014, Vol 14, Issue 6, p10578
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s140610578