Memorias de investigación
Communications at congresses:
Region-dependent Vehicle Classification Using PCA Features
Year:2012
Research Areas
  • Engineering,
  • Processing and signal analysis
Information
Abstract
Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.
International
Si
Congress
IEEE Int. Conf. on Image Processing
960
Place
Orlando (FL), USA
Reviewers
Si
ISBN/ISSN
978-1-4673-2534-9
10.1109/ICIP.2012.6466894
Start Date
30/09/2012
End Date
03/10/2012
From page
453
To page
456
Proc. of IEEE Int. Conf. on Image Processing, ICIP 2012
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)