Citation

Parra I, Llorca DF, Sotelo MA, Bergasa LM, Garrido MAG, Ocana M, Revenga de Toro P, Nuevo J. IEEE Trans. Intel. Transp. Syst. 2007; 8(2): 292-307.

Copyright

(Copyright © 2007, IEEE (Institute of Electrical and Electronics Engineers))

DOI

unavailable

PMID

unavailable

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.