3D-Guided Multiscale Sliding Window for Pedestrian Detection

The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification, where the former aims at presenting image windows to be classified as containing a pedestrian or not. Much attention has been paid to the classification module while candidate generation has mainly relied on (multiscale) sliding window pyramid. In this paper, we assume a context of autonomous driving based on stereo vision and evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundreds of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multi-modal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM.