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.
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Clady, X., Negri, P., Milgram, M., Poulenard, R. (2008). Multi-class Vehicle Type Recognition System. In: Prevost, L., Marinai, S., Schwenker, F. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2008. Lecture Notes in Computer Science(), vol 5064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69939-2_22
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DOI: https://doi.org/10.1007/978-3-540-69939-2_22
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