Human detection using Discriminative and Robust Local Binary Pattern
Despite superior performance of Local Binary Pattern (LBP) in texture classification and face detection, its performance in human detection has been limited for two reasons. Firstly, LBP differentiates a bright human from a dark background and vice-versa. This increases the intra-class variation of humans. Secondly, LBP is contrast and illumination invariant. It does not discriminate between weak contrast local regions and similar strong contrast ones, resulting in a similar feature representation. Non-Redundant LBP (NRLBP) has been proposed to solve the first issue of LBP. However, an inherent limitation of NRLBP is that LBP codes and their complements in the same block are mapped to the same code. Furthermore, NRLBP, like LBP, is also contrast and illumination invariant. In this paper, we propose a novel edge-texture feature, Discriminative Robust Local Binary Pattern (DRLBP), for human detection. DRLBP alleviates the problems of LBP and NRLBP by considering the weighted sum and absolute difference of a LBP code and its complement. Our experimental results show that DRLBP consistently outperforms LBP and NRLBP for human detection.