Local Binary Pattern features for pedestrian detection at night/dark environment

Abstract

Being fast to compute and simple to implement, Local Binary Pattern (LBP) has also shown superior performance in texture classification and face detection. However, it is not well optimized for pedestrian detection. At night/dark environment, pedestrian detection typically needs to overcome problems of low contrast, image blur, and image noise. A novel feature extraction method, consisting of Weighted LBP, Multi-resolution LBP, and Multi-scale LBP, is proposed to solve them. Experimental results show that the proposed method improves upon the basic LBP significantly and outperforms benchmarks such as HOG and CoHOG.