Basis Mapping Based Boosting for Object Detection

We propose a novel mapping method to improve the training accuracy and efficiency of boosted classifiers for object detection. The key step of the proposed method is a non linear mapping on original samples by referring to the basis samples before feeding into the weak classifiers, where the basis samples correspond to the hard samples in the current training stage. We show that the basis mapping based weak classifier is an approximation of kernel weak classifiers while keeping the same computation cost as linear weak classifiers. As a result, boosting with such weak classifiers is more effective. In this paper, two different non-linear mappings are shown to work well. We adopt the LogitBoost algorithm to train the weak classifiers based on the Histogram of Oriented Gradient descriptor (HOG). Experimental results show that the proposed approach significantly improves the detection accuracy and training efficiency of the boosted classifier. It also achieves performance comparable with the commonly used methods on public datasets for both pedestrian detection and general object detection tasks.