Open Access

A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection

  • Pablo Negri1,
  • Xavier Clady1,
  • Shehzad Muhammad Hanif1 and
  • Lionel Prevost1Email author
EURASIP Journal on Advances in Signal Processing20082008:782432

Received: 1 October 2007

Accepted: 16 January 2008

Published: 18 February 2008


We present an algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results of the generative (HoG features), discriminative (Haar-like features) detectors, and of their fusion is presented. These results show that the fusion combines the advantages of the other two detectors: generative classifiers eliminate "easily" negative examples in the early layers of the cascade, while in the later layers, the discriminative classifiers generate a fine decision boundary removing the negative examples near the vehicle model. The best algorithm achieves good performances on a test set containing some 500 vehicle images: the detection rate is about 94% and the false-alarm rate per image is 0.0003.

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Authors’ Affiliations

Institut des Systèmes Intelligents et de Robotique, CNRS FRE 2507, Université Pierre et Marie Curie-Paris 6


© Pablo Negri et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.