Turnout detection and classification using a modified HOG and template matching

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

1 Siemens Mobility
2 CAOR - Centre de Robotique

— This paper presents a railway track and turnout detection and turnout classification algorithm. 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 knowing their gauge. The turnout detection scheme is based on the Histogram of Oriented Gradient (HOG) and Template Matching (TM). The turnout classification is based on HOG. The detection results show (i) reliable performance for our railway track extraction scheme; (ii) a correction rate of 97.31 percent for the turnout detection scheme using a Support Vector Machine (SVM) classifier. The turnout classification has correction rate of 98.72 percent using SVM.

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

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

Jorge Corsino Espino, Bogdan Stanciulescu. Turnout detection and classification using a modified HOG and template matching. Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on, IEEE, Oct 2013, The Hague, Netherlands. pp.2045 - 2050, ⟨10.1109/ITSC.2013.6728530⟩. ⟨hal-01517892⟩