Title :
Vehicle categorization: parts for speed and accuracy
Author :
Nowak, Eric ; Jurie, Frédéric
Author_Institution :
Laboratoire GRAVIR /UMR 5527 du CNRS-INRIA Rhone-Alpes-UJF-INPG and Societe Bertin - Technologies, Aix-en-Provence. eric.nowak@inrialpls.fr
Abstract :
In this paper we propose a framework for categorization of different types of vehicles. The difficulty 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 classifiers 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.
Keywords :
image classification; vehicles; feature selection scheme; high-level data transformation algorithm; infrared surveillance cameras; part-based object classification; part-based recognition system; vehicle categorization; Cameras; Classification tree analysis; Detectors; Infrared surveillance; Object detection; Quantization; Support vector machine classification; Support vector machines; Testing; Vehicles;
Conference_Titel :
Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on
Print_ISBN :
0-7803-9424-0
DOI :
10.1109/VSPETS.2005.1570926