Turnout detection and classification using a modified HOG and template matching

Author

Espino, Jorge Corsino ; Stanciulescu, Bogdan

Author_Institution

Div. Mobility & Logistics, SIEMENS S.A.S. Infrastruct. & Cities, Chatillon, France

fYear

2013

fDate

6-9 Oct. 2013

Firstpage

2045

Lastpage

2050

Abstract

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.

Keywords

edge detection; feature extraction; gradient methods; image classification; image matching; object detection; railways; support vector machines; RANSAC algorithm; SVM; TM; edge detection; histogram of oriented gradient; modified HOG; railway track extraction; support vector machine classifier; template matching; turnout classification; turnout detection; Cameras; Correlation; Image edge detection; Mathematical model; Rail transportation; Rails; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems - (ITSC), 2013 16th International IEEE Conference on

Conference_Location

The Hague

Type

conf

DOI

10.1109/ITSC.2013.6728530

Filename

6728530