Rail and turnout detection using gradient information and template matching

Jorge Corsino Espino (1) , Bogdan Stanciulescu (2) , Philippe Forin (1)

1 Siemens Mobility
2 CAOR - Centre de Robotique

—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.

Mots clés: support vector machines; edge detection; feature extraction; image classification; image matching; iterative methods; rails; railways

Domaines: Informatique [cs]; Robotique [cs.RO]; Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Informatique [cs]; Systèmes embarqués; Informatique [cs]; Traitement des images [eess.IV]

Jorge Corsino Espino, Bogdan Stanciulescu, Philippe Forin. Rail and turnout detection using gradient information and template matching. Intelligent Rail Transportation (ICIRT), 2013 IEEE International Conference on, IEEE, Aug 2013, Beijing, China. pp.233 - 238, ⟨10.1109/ICIRT.2013.6696299⟩. ⟨hal-01517885⟩