Stagged multi-scale LBP for pedestrian detection

Pedestrian detection remains a popular and challenging problem due to large variation in appearance. A robust feature extraction method is highly desired for accurate pedestrian detection. In this paper, firstly, we propose a staggered multiscale LBP histogram. In order to exploit grayscale difference information in more directions, three scales with radius of 1, 3, and 5 pixels are utilized, and different scales are staggered. The Staggered Multi-scale LBP histogram is composed of three 256-bin histograms, each of which corresponds to one of the three scales. Secondly, dimensionality of the LBP histogram is reduced using a boosting learning method. Experimental results show that the proposed feature outperforms benchmarks such as Uniform-LBP, HOG and CoHOG on INRIA, Daimler Chrysler and our Panasonic night time datasets.