Memorias de investigación
Communications at congresses:
HOG-Like Gradient-Based Descriptor for Visual Vehicle Detection
Year:2012
Research Areas
  • Engineering
Information
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%.
International
Si
Congress
IEEE Intelligent Vehicles Symposium
960
Place
Alcalá de Henares, Spain
Reviewers
Si
ISBN/ISSN
978-1-4673-2119-8
10.1109/IVS.2012.6232119
Start Date
03/06/2012
End Date
07/06/2012
From page
223
To page
228
Proc. of IEEE Intelligent Vehicles Symposium, IV 2012
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)