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Détection de rails, caractérisation de croisements et localisation de trains sur la trajectoire d'un métro automatique

Abstract : This thesis deals with obstacle detection in a railway setting using computer vision. The main task is to provide perception of the environment in front ofthe train using an optical sensor to detect and evaluate distances to obstacles along the track path.We present a module for detecting rails from grayscale images to determine an obstacle-free zone in front of the train. This detection is based on the RANSACalgorithm and fitting the track to a second degree polynomial. The method has shown itself robust to our dataset and allows detecting the rails at distancesgreater than the emergency stopping distance. In addition, a method for calibrating the cameras installed on the train is proposed based on the morphology ofthe track.To supplement rail detection, we present a new module for detecting and classifying junctions based on the HOG descriptor extracted from InversePerspective Mapping (IPM) images. A Support Vector Machines (SVM) binary classifier was used for detection and a multi-class SVM for distinguishing ofjunctions along the rails.In the sequel, a train detector was implemented. Using a set of images of trains found on the studied line and negative images such as cars or buses, we havecreated a database of obstacles to find a robust descriptor which is able to model the form of trains and allows a SVM classifier to distinguish images anddetect trains. Next this classifier is used by the overall system to determine the presence of a train in addition to detecting rails. At the maximum detectiondistance, a train-sized rectangle is extracted from the image in order to confirm the presence of a train. These rectangles are classified by means of HOG-typeglobal descriptors and a binary SVM structure.In addition to its applications to concrete problems, this study permits to evaluate the maturity of image processing technologies forfail-safe railway systems.
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Submitted on : Friday, September 26, 2014 - 2:48:11 PM
Last modification on : Wednesday, November 17, 2021 - 12:31:01 PM
Long-term archiving on: : Saturday, December 27, 2014 - 11:30:16 AM


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  • HAL Id : pastel-01068899, version 1


Jorge Corsino Espino. Détection de rails, caractérisation de croisements et localisation de trains sur la trajectoire d'un métro automatique. Autre [cs.OH]. Ecole Nationale Supérieure des Mines de Paris, 2014. Français. ⟨NNT : 2014ENMP0013⟩. ⟨pastel-01068899⟩



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