In response to the slow running speed of the Deformable Part Model algorithm in the process of the pedestrian detection, this paper incorporated Cascade Detection algorithm and Branch-and-Bound algorithm into a fast pedestrian detection algorithm which is based on Deformable Part Model. In a pedestrian detection process, a sequence model evaluates individual parts sequentially to quickly prune most of the smaller possible objects. This aims to accelerate the process of object positioning, and to optimize global classification results in all possible image regions. Meanwhile, the boundaries of the maximum are adopted to search the clipping operation of the window. In order to improve detection speed without compromising the accuracy of the detection, this paper increase the number of the part models involved. According to the experimental results on INRIA data set, the proposed algorithm successfully improved the accuracy and the speed of detection.