Region-dependent vehicle classification using PCA features

Arróspide Laborda, Jon y Salgado Álvarez de Sotomayor, Luis (2012). Region-dependent vehicle classification using PCA features. En: "19th IEEE International Conference on Image Processing (ICIP)", 30/09/2012 - 03/10/2012, Orlando, Florida, EE.UU. pp. 453-456. https://doi.org/10.1109/ICIP.2012.6466894.

Descripción

Título: Region-dependent vehicle classification using PCA features
Autor/es:
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 19th IEEE International Conference on Image Processing (ICIP)
Fechas del Evento: 30/09/2012 - 03/10/2012
Lugar del Evento: Orlando, Florida, EE.UU
Título del Libro: 19th IEEE International Conference on Image Processing (ICIP)
Fecha: 2012
Materias:
Palabras Clave Informales: Intelligent vehicles, Hypothesis verification, Principal component analysis, Machine learning, Vehicle database
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

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.

Más información

ID de Registro: 30497
Identificador DC: http://oa.upm.es/30497/
Identificador OAI: oai:oa.upm.es:30497
Identificador DOI: 10.1109/ICIP.2012.6466894
Depositado por: Memoria Investigacion
Depositado el: 10 Ago 2014 10:12
Ultima Modificación: 22 Abr 2016 00:46
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