A cascade of boosted generative and discriminative classifiers for vehicle detection

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. Copyright (c) 2008 Pablo Negri et al.