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

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) the authors use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) the authors use a linear regression technique to predict the position and length of an object from image processing; (3) the authors 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.

• Record URL:
  • https://doi.org/10.3390/s140610578
• Record URL:
  • http://www.mdpi.com/1424-8220/14/6/10578
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2014 Wen-Yuan Chen et al.
• Authors:
  • Chen, Wen-Yuan
  • Wang, Mei
  • Fu, Zhou-Xing
• Publication Date: 2014-6

Media Info

• Media Type: Web
• Features: Figures; Photos; References; Tables;
• Pagination: pp 10578-10597
• Serial:
  • Sensors
  • Volume: 14
  • Issue Number: 6
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Detection and identification; Image processing; Linear regression analysis; Railroad crashes; Railroad grade crossings; Railroad safety; Risk assessment
• Subject Areas: Data and Information Technology; Highways; Railroads; Safety and Human Factors; I85: Safety Devices used in Transport Infrastructure;

Filing Info

• Accession Number: 01539203
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 26 2014 2:29PM