HOG-Like gradient-based descriptor for visual vehicle detection

Arróspide Laborda, Jon; Salgado Álvarez de Sotomayor, Luis y Marinas Mateos, Javier (2012). HOG-Like gradient-based descriptor for visual vehicle detection. En: "IEEE Intelligent Vehicles Symposium (IV)", 03/06/2012 - 07/06/2012, Alcalá de Henares, Spain. pp. 223-228. https://doi.org/10.1109/IVS.2012.6232119.

Descripción

Título: HOG-Like gradient-based descriptor for visual vehicle detection
Autor/es:
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
  • Marinas Mateos, Javier
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: IEEE Intelligent Vehicles Symposium (IV)
Fechas del Evento: 03/06/2012 - 07/06/2012
Lugar del Evento: Alcalá de Henares, Spain
Título del Libro: IEEE Intelligent Vehicles Symposium (IV)
Fecha: 2012
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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%.

Más información

ID de Registro: 30493
Identificador DC: http://oa.upm.es/30493/
Identificador OAI: oai:oa.upm.es:30493
Identificador DOI: 10.1109/IVS.2012.6232119
Depositado por: Memoria Investigacion
Depositado el: 09 Ago 2014 12:41
Ultima Modificación: 22 Abr 2016 00:46
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