Memorias de investigación
Ponencias en congresos:
HOG-Like Gradient-Based Descriptor for Visual Vehicle Detection
Año:2012
Áreas de investigación
  • Ingenierías
Datos
Descripción
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
Si
Nombre congreso
IEEE Intelligent Vehicles Symposium
Tipo de participación
960
Lugar del congreso
Alcalá de Henares, Spain
Revisores
Si
ISBN o ISSN
978-1-4673-2119-8
DOI
10.1109/IVS.2012.6232119
Fecha inicio congreso
03/06/2012
Fecha fin congreso
07/06/2012
Desde la página
223
Hasta la página
228
Título de las actas
Proc. of IEEE Intelligent Vehicles Symposium, IV 2012
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)