Pedestrian detection via part-based topology model
In this paper, we propose a part-based topology model and a pedestrian detection method, which obviously improve the detection accuracy. In Our method, pedestrian is divided into several parts. Firstly, histogram of oriented gradients (HOG) features and linear support vector machine (SVM) classifier are used to detect pedestrian parts. Secondly, a novel binary descriptor called log-polar pattern (LPP) is proposed to represent the spatial relation of a part pair. Then multiple LPPs are combined as a log-polar topology pattern (LTP) to model the global topology of a pedestrian. Finally, we put the LTP into One-Class SVM (OC-SVM) to determine whether the detected parts indicate a pedestrian or not. Experiments in INRIA dataset show that our method is robust to occlusion and multi-postures, which obviously reduces the miss rate.