@article{ref1,
title="Combination of Feature Extraction Methods for SVM Pedestrian Detection",
journal="IEEE transactions on intelligent transportation systems",
year="2007",
author="Parra, I and Llorca, DF and Sotelo, MA and Bergasa, LM and Garrido, Miguel Aangel Garcia and Ocana, M and Revenga de Toro, P and Nuevo, J",
volume="8",
number="2",
pages="292-307",
abstract="In this paper the authors provide an automated feature extraction mechanism using stereovision and a support vector machine (SVM) for pedestrian detection in Intelligent Transportation Systems (ITS). A monocular detection method is used, as such systems are more easily produced en mass for market purposes. Pedestrian detection is also difficult for such applications because it is conducted from a moving vehicle, must quantify and select many candidates in large groups called multi-candidate (MC) generation, and must be able to maintain such functionality with or without full daylight. In order to reduce the number of false candidates in a given group the model also employs subtractive clustering. The authors propose that, for future projects, a more robust motion-based and position-dependent incorporative mechanism is used to enhance the detection method presented here.<p />",
language="",
issn="1524-9050",
doi="",
url="http://dx.doi.org/"
}