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

Sensors

Wen-Yuan Chen¹  Mei Wang¹ 

[1] Department of Electronic Engineering, National Chin-Yi University of Technology 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan; E-Mail:

关键词: railway crossing;    object extraction;    background subtraction;    linear regression;   

DOI  :  10.3390/s140610578

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.