Region-dependent vehicle classification using PCA features

Arróspide Laborda, Jon and Salgado Álvarez de Sotomayor, Luis (2012). Region-dependent vehicle classification using PCA features. In: "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.

Description

Title: Region-dependent vehicle classification using PCA features
Author/s:
  • Arróspide Laborda, Jon
  • Salgado Álvarez de Sotomayor, Luis
Item Type: Presentation at Congress or Conference (Article)
Event Title: 19th IEEE International Conference on Image Processing (ICIP)
Event Dates: 30/09/2012 - 03/10/2012
Event Location: Orlando, Florida, EE.UU
Title of Book: 19th IEEE International Conference on Image Processing (ICIP)
Date: 2012
Subjects:
Freetext Keywords: Intelligent vehicles, Hypothesis verification, Principal component analysis, Machine learning, Vehicle database
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

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.

More information

Item ID: 30497
DC Identifier: http://oa.upm.es/30497/
OAI Identifier: oai:oa.upm.es:30497
DOI: 10.1109/ICIP.2012.6466894
Deposited by: Memoria Investigacion
Deposited on: 10 Aug 2014 10:12
Last Modified: 22 Apr 2016 00:46
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