PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Vehicle Categorization: Parts for Speed and Accuracy
Eric Nowak and Frederic Jurie
VS-PETS workshop, in conjuction with ICCV 05 - 2005 2005.

Abstract

In this paper we propose a framework for categorization of different types of vehicles. The difculty comes from the high inter-class similarity and the high intra-class variability. We address this problem using a part-based recognition system. We particularly focus on the trade-off between the number of parts included in the vehicle models and the recognition rate, i.e the trade-off between fast computation and high accuracy. We propose a high-level data transformation algorithm and a feature selection scheme adapted to hierarchical SVM classiers to improve the performance of part-based vehicle models. We have tested the proposed framework on real data acquired by infrared surveillance cameras, and on visible images too. On the infrared dataset, with the same speedup factor of 100, our accuracy is 12% better than the standard one-versus-one SVM.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1741
Deposited By:Frederic Jurie
Deposited On:28 November 2005