Object detection method based on local kernels and automatic kernel selection by Kullback-Leibler divergence

This paper presents a object detection method based on local kernels. The local kernels are arranged to all positions on recognition target and are selected automatically by using Kullback-Leibler divergence according to the recognition target. The proposed method is applied to pedestrian detection problem. The performance of the proposed method is evaluated by the experiment using MIT CBCL pedestrian database. It is confirmed that generalization ability of the proposed method is improved by selecting the local kernels automatically.