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A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection

Abstract

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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Correspondence to Lionel Prevost.

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Negri, P., Clady, X., Hanif, S.M. et al. A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection. EURASIP J. Adv. Signal Process. 2008, 782432 (2008). https://doi.org/10.1155/2008/782432

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Keywords

  • Full Article
  • Vehicle Model
  • Decision Boundary
  • Detection Problem
  • Good Algorithm
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