Multi-class Vehicle Type Recognition System

Authors:
Xavier Clady;Pablo Negri;Maurice Milgram;Raphael Poulenard
Affiliations:
Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie-Paris 6, CNRS FRE 2907,;Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie-Paris 6, CNRS FRE 2907,;Institut des Systèmes Intelligents et Robotique, Université Pierre et Marie Curie-Paris 6, CNRS FRE 2907,;LPR Editor - Montpellier,
Venue:
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Year:
2008

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Abstract

This paper presents a framework for multiclass vehicle type (Make and Model) identification based on oriented contour points. A method to construct a model from several frontal vehicle images is presented. Employing this model, three voting algorithms and a distance error allows to measure the similarity between an input instance and the data bases classes. These scores could be combined to design a discriminant function. We present too a second classification stage that employ scores like vectors. A nearest-neighbor algorithm is used to determine the vehicle type. This method have been tested on a realistic data set (830 images containing 50 different vehicle classes) obtaining similar results for equivalent recognition frameworks with different features selections [12]. The system also shows to be robust to partial occlusions.