Evaluation of local feature descriptors and their combination for pedestrian representation

Pedestrian detection problem has been a touchstone of various image feature descriptors. In this paper, we evaluate four kinds of representative local descriptors (HOG, Haar-like, SURF and LBP) for pedestrian representation. Our goal is to find out the best combination of feature descriptors by analyzing and evaluating the complementarities of them. With the cross validation method, we first find out the best descriptor, which is then combined with other descriptors one by one for evaluation. In addition to direct descriptor combination, we propose a new descriptor strategy, called structural combination. Experiments on two public pedestrian datasets show that the performance evaluation can support the complementarily analysis and the complementarities is relevant to combination strategies.