Multi-view vehicle detection in traffic surveillance combining HOG-HCT and deformarle part models

This paper presents a robust multi-view vehicle detection based on the Histogram of Oriented Gradient (HOG)-Histograms of Census Transform (HCT) features and the mixtures of deformable part models. As some virtual features of vehicle in single view, such as headlight, taillight and edges can not been directly used, we develop a new HOG-HCT feature to describe the vehicle structure feature in multi-view. The HCT feature, inspired by the success of HOG in object detection, is obtained by the same strategy of HOG to the census transform value and we use the Principal Component Analysis (PCA) to fuse HOG and HCT to get the HOG-HCT feature. At last, we apply the deformable part models with the HOG-HCT feature to our training set and gain three view models. Experimental results show that the proposed method is very powerful in detecting vehicles under traffic surveillance environment.