Multi-class Vehicle Type Recognition System

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.

• Type: Conference
• Year: 2008
• Where: ANNPR
• Authors: Xavier Clady, Pablo Negri, Maurice Milgram, Raphael Poulenard
• Keywords: ANNPR 2008, Frontal Vehicle Images, Multiclass Vehicle Type, Neural Networks, Vehicle Type