Heterogeneous AdaBoost with Real-time Constraints - Application to the Detection of Pedestrians by Stereovision

This paper presents a learning based method for pedestrians detection, combining appearance and depth map descriptors. Recent works have presented the added value of this combination. We propose two contributions: 1) a comparative study of various depth descriptors including a fast descriptor based on average depth in a sub-window of the tested area and 2) an adaptation of the Adaboost algorithm in order to handle heterogeneous descriptors in terms of computational cost. Our goal is to build a detector balancing detection rate and execution time. We show the relevance of the proposed algorithm on real video data.