Vehicle Make and Model Identification Using Vision System. Reconnaissance par Vision du Type d’un Véhicule Automobile

Vehicle Make and Model Identification Using Vision System

Reconnaissance par Vision du Type d’un Véhicule Automobile

X. Clady P. Negri  M. Milgram  R. Poulenard 

Université Pierre et Marie Curie-Paris 6, Institut des Systèmes Intelligents et de Robotique – CNRS, 4, place Jussieu, 75005 Paris, France

LPREditor, Montpellier, France

Page: 
31-46
|
Received: 
11 March 2008
|
Accepted: 
N/A
|
Published: 
28 February 2009
| Citation

OPEN ACCESS

Abstract: 

With the first strategy, the system correctly identifies 80,2% of 830 test samples. The mean of the recognition rates per class is 69,4%. The second strategy obtains better results (in mean, with 100 randomly different repartitions). In the first space, wf, we obtain 93,1% for the correctly identification rate (83,5% for the mean of the recognition rates per class). In a second space, PC A50 wf , we obtain 86,2% for the correctly identification rate (78,8% for the mean of the recognition rates per class). In the last space f, we obtain 90,6% for the correctly identification rate (86,4% for the mean of the recognition rates per class). Another test simulates the presence of a tollgate at four different locations. The better recognition are obtained if the virtual tollgate hides the upper part of the images: a lot of noise points are extracted from this part. These points perturb the recognition system. They are filtered if the number of images used in the model creation is sufficient (> 5). The results presented allow to conlude that the second strategy obtains better results, particularly if the fusion scores are used.

Résumé

Cet article présente un système de reconnaissance du type (constructeur, modèle) de véhicules par vision. À partir d’une vue de face avant d’un véhicule, limitée à sa calandre, nous en construisons une représentation à base de points de contour orientés. La classification est réalisée essentiellement en se fondant sur des algorithmes de votes. L’utilisation d’algorithmes de votes permet au système d’être robuste aux données manquantes ou erronées de la représentation. Nous avons donc construit une fonction de discrimination qui combine 3 votes et une distance, et agit comme une mesure de similarité entre chaque modèle et l’image de véhicule testée. Deux stratégies de décision ont été testées. La première associe à une image de calandre avant du véhicule, le modèle qui a obtenu la valeur la plus importante en sortie de la fonction. Une seconde stratégie regroupe toutes les sorties en un vecteur. La décision est alors prise via un algorithme de plus proche voisin dans un espace dit de votes. Avec la première stratégie, un taux de reconnaissance de 93 % est obtenu sur une base d’images prises en conditions réelles composée de 20 classes de type de véhicules. De plus, une caractérisation et une analyse du fonctionnement du système vis-à-vis de ses différents paramètres est proposée. Cependant ce taux chute à 80 % lorsque le nombre de modèles passe à 50 classes. Pour le même nombre de classes, la seconde stratégie permet d’obtenir un taux supérieur à 90 %.

Keywords: 

Pattern Recognition, Vision, Multiclass recognition, Voting Algorithm.

Mots clés

Reconnaissance des formes, vision, classification multi-classes, méthode de votes.

1. Introduction
2. Contexte de l’étude
3. Création du Modèle
4. Classification
5. Résultats et Caractérisation
6. Conclusions
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