Rail and turnout detection using gradient information and template matching

Author

Corsino Espino, Jorge ; Stanciulescu, Bogdan ; Forin, Philippe

Author_Institution

SWE RC-FR IC-MOL RA R&D, SIEMENS S.A.S., Chatillon, France

fYear

2013

fDate

Aug. 30 2013-Sept. 1 2013

Firstpage

233

Lastpage

238

Abstract

This paper presents a railway track and turnout detection algorithm which is not based on an empirical threshold. The railway track extraction is based on an edge detection using the width of the rolling pads. This edge detection scheme is then used as an input to the RANSAC algorithm to determine the model of the rails. The turnout detection scheme is based on the Histogram of Oriented Gradient (HOG) and Template Matching (TM). The results show (i) reliable performance for our railway track extraction scheme and (ii) a correction rate of 97.31 percent for the turnout detection scheme using a Support Vector Machine (SVM) classifier.

Keywords

edge detection; feature extraction; image classification; image matching; iterative methods; rails; railways; support vector machines; HOG; RANSAC algorithm; SVM classifier; TM; correction rate; edge detection; gradient information; histogram-of-oriented gradient; rail detection; railway track detection algorithm; railway track extraction scheme; rolling pad width; support vector machine classifier; template matching; turnout detection scheme; Cameras; Correlation; Image edge detection; Lighting; Rail transportation; Rails; Support vector machines; Rails detection; Turnout detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Rail Transportation (ICIRT), 2013 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-5278-9

Type

conf

DOI

10.1109/ICIRT.2013.6696299

Filename

6696299