Memorias de investigación
Ponencias en congresos:
Region-dependent Vehicle Classification Using PCA Features
Año:2012
Áreas de investigación
  • Ingenierías,
  • Procesado y análisis de la señal
Datos
Descripción
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.
Internacional
Si
Nombre congreso
IEEE Int. Conf. on Image Processing
Tipo de participación
960
Lugar del congreso
Orlando (FL), USA
Revisores
Si
ISBN o ISSN
978-1-4673-2534-9
DOI
10.1109/ICIP.2012.6466894
Fecha inicio congreso
30/09/2012
Fecha fin congreso
03/10/2012
Desde la página
453
Hasta la página
456
Título de las actas
Proc. of IEEE Int. Conf. on Image Processing, ICIP 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)