HOG-Like gradient-based descriptor for visual vehicle detection

Arróspide Laborda, Jon and Salgado Álvarez de Sotomayor, Luis and Marinas Mateos, Javier (2012). HOG-Like gradient-based descriptor for visual vehicle detection. In: "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.

Description

Title: HOG-Like gradient-based descriptor for visual vehicle detection
Author/s:
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
  • Marinas Mateos, Javier
Item Type: Presentation at Congress or Conference (Article)
Event Title: IEEE Intelligent Vehicles Symposium (IV)
Event Dates: 03/06/2012 - 07/06/2012
Event Location: Alcalá de Henares, Spain
Title of Book: IEEE Intelligent Vehicles Symposium (IV)
Date: 2012
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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%.

More information

Item ID: 30493
DC Identifier: http://oa.upm.es/30493/
OAI Identifier: oai:oa.upm.es:30493
DOI: 10.1109/IVS.2012.6232119
Deposited by: Memoria Investigacion
Deposited on: 09 Aug 2014 12:41
Last Modified: 22 Apr 2016 00:46
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