Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 153-156.doi: 10.11896/JsJkx.200100008

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Rail Area Extraction Using Extended Haar-like Features and DBSCAN Clustering

LUO Jin-nan and ZHANG Ji-min   

  1. Institute of Rail Transit,TongJi University,Shanghai 201804,China
  • Published:2020-07-07
  • About author:LUO Jin-nan, postgraduate.His main research interests include rolling stock intelligent control and active safety protection.
    ZHANG Ji-min, born in 1969, Ph.D, professor.His main research interests include rolling stock dynamics, mechatronic system design and rolling stock intelligent control.

Abstract: Obstacle is a potential threat to the normal operation of trains.Rail area extraction is a key step in the process of using the train’s forward-looking camera to detect obstacles.Rail area extraction algorithm needs to be able to quickly and effectively detect the position of the rail while not occupying too much computing resources to keep the normal calculation speed of the obstacle recognition algorithm.This paper proposes a rail area extraction algorithm based on extended Haar-like feature extraction and DBSCAN density clustering.Firstly,the image is preprocessed by algorithms such as affine transformation,pooling,gray level equalization,and edge detection.Then the feature points of the rail are extracted based on multiple extended Haar-like features.Finally,the DBSCAN algorithm is used to extract valid feature data points and curve fitting is performed through these points.The experimental result shows that the algorithm can effectively detect the position of the rail area during the running of the train,and meet the practical needs of multiple scenarios and conditions

Key words: DBSCAN clustering, Extended Haar-like feature, ObJect detection, Rail area extraction, Rail tranist

CLC Number: 

  • U298
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