Association of AdaBoost and Kernel based machine learning methods for visual pedestrian recognition

We present a real-time solution for pedestrian detection in images. The key point of such method is the definition of a generic model able to describe the huge variability of pedestrians. We propose a learning based approach using a training set composed by positive and negative samples. A simple description of each candidate image provides a huge feature vector from which can be built weak classifiers. We select a subset of relevant weak classifiers using a classic AdaBoost algorithm. The resulting subset is then used as binary vectors in a kernel based machine learning classifier (like SVM, RVM, ...). The major contribution of the paper is the original association of an AdaBoost algorithm to select the relevant weak classifiers, followed by a SVM like classifier for which input data are given by the selected weak classifiers. Kernel based machine learning provides non-linear separator into the weak classifier space while standard AdaBoost gives a linear one. Performances of this method are compared to state of art methods and a real-time application with a monocular camera embedded in a moving vehicle is also presented to match this approach against a real context.