Descripción
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One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%. | |
Internacional
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Si |
Nombre congreso
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IEEE Intelligent Vehicles Symposium |
Tipo de participación
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960 |
Lugar del congreso
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Alcalá de Henares, Spain |
Revisores
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Si |
ISBN o ISSN
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978-1-4673-2119-8 |
DOI
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10.1109/IVS.2012.6232119 |
Fecha inicio congreso
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03/06/2012 |
Fecha fin congreso
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07/06/2012 |
Desde la página
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223 |
Hasta la página
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228 |
Título de las actas
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Proc. of IEEE Intelligent Vehicles Symposium, IV 2012 |