A comparative study of pedestrian detection methods using classical Haar and HoG features versus bag of words model computed from Haar and HoG features

The bag of words model has been actively adopted by content based image retrieval and image annotation techniques. We employ this model for the particular task of pedestrian detection in two dimensional images, producing this way a novel approach to pedestrian detection. The experiments we have done in this paper compare the behavior of discriminative recognition approaches that use AdaBoost on codebook features versus Adaboost trained on primitive features that may be extracted from a two dimensional image. By primitive features we refer in this paper to Haar features and Histogram of Oriented Gradients both being extremely used in object recognition in general and in pedestrian detection in particular. The conclusion of our experiments is that the codebook representation performs better than the primitive feature representation.