A cascade of boosted generative and discriminative classifiers for vehicle detection

Authors:
Pablo Negri;Xavier Clady;Shehzad Muhammad Hanif;Lionel Prevost
Affiliations:
Institut des Systèmes Intelligents et de Robotique, CNRS FRE, Université Pierre et Marie Curie-Paris, Ivry sur Seine, France;Institut des Systèmes Intelligents et de Robotique, CNRS FRE, Université Pierre et Marie Curie-Paris, Ivry sur Seine, France;Institut des Systèmes Intelligents et de Robotique, CNRS FRE, Université Pierre et Marie Curie-Paris, Ivry sur Seine, France;Institut des Systèmes Intelligents et de Robotique, CNRS FRE, Université Pierre et Marie Curie-Paris, Ivry sur Seine, France
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

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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.