受韦伯局部描述子和局部二值模式（ LBP ）特征的启发，针对 Haar 特征维度高、冗余度大等缺点，提出了一种基于显著性的二值化 Haar 特征（ SLBH ） . 虽然利用整体行人特征能取得较好的检测效果，但其检测性能在遮挡场景中会迅速下降 . 为提高整体特征对部分遮挡的鲁棒性，文中提出了一种结合 SLBH 特征多部件验证的双层行人检测算法 . 该算法结合了整体特征与局部特征的优点，增强了算法对部分遮挡的鲁棒性 . 在 INRIA 行人检测库上的实验结果表明，文中提出的算法对噪声和部分遮挡有较好的鲁棒性.

Inspired by Weber's local descriptor and LBP feature ， this paper proposes a SLBH (Saliency Local Binary Haar) feature in view of the weaknesses that Haar features have high dimension and redundancy. SLBH helps obtain good detection performance when using the overall characteristics of pedestrian ， but the detection performance declines rapidly in occlusion scenes. In order to improve the robustness of overall characteristics to partial occlusion ， a two-stage pedestrian detection algorithm combining multi-component validation and SLBH is proposed ， which takes the advantage of overall feature and local feature simultaneously ， and improves the robustness of the algorithm to partial occlusion. Experimental results on INRIA pedestrian detection dataset show that the proposed algorithm is of strong robustness to noise and partial occlusion.