Abstract: Pedestrian detection is a hot topic in computer vision and it includes three parts: feature extraction, classification, and non maximum suppression (NMS). Most of the existing results on pedestrian detection have focuses on feature extraction. feature learning, and classifier. To the contrary, there exist few results on NMS. Moreover, the common approach on NMS is based on greedy strategy, which only uses the information of overlapping area to suppress other windows. By means of ACF (Integral Channel Features), this paper proposes three improved NMS algorithms, which can notably raise the accuracy of computation without increasing computation time. On the data set of INRIA pedestrian, when only using the dynamic overlap threshold changing with the scale rate, the MR (log average miss rate) can be reduce by 0.99%, which can be only reduced by 1.25% when using the strategy of saving outlying detection windows with the similar score. The integration of these two methods can reduce MR by 2.5%. Furthermore, the suppression again of the suppressed detection windows can further reduce MR by 2.63% to achieve a lower MR with 14.22%.