A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection. (13th April 2008)

• Record Type: Journal Article
• Authors: Negri Negri, Pablo Pablo; Clady Clady, Xavier Xavier; Hanif Hanif, Shehzad Muhammad Shehzad Muhammad; Prevost Prevost, Lionel Lionel
• Other Names: Cardot Cardot Hubert Hubert Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-04-13
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/782432
• Languages: English
• ISSNs: 1687-6172

Abstract

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.