The detection accuracy of the Deformable Part Model (DPM) algorithm is high in the field of pedestrian detection, however, in the two steps of feature extraction and pedestrian location, the computation is too large, which leads to the slow detection speed and the deformable part model algorithm can not be used in real time pedestrian detection. To solve the problems, a deformable Part Model with Branch and Bound (BB) algorithm and Cascaded Detection (CD) algorithm (BBCDPM) was proposed. First, the Histogram of Oriented Gradients (HOG) feature was selected to describe human target to generate characteristic pyramid. Then, the deformable part model was modeled, and the Latent Support Vector Machine (LSVM) was used to train the model. In order to increase the accuracy of pedestrian detection, the part model of traditional deformation part model algorithm was increased from 5 to 8 parts. Finally, the cascade detection algorithm was used to simplify detection model, then the maximum value was found by combining with the branch and bound algorithm, and a lot of impossible object assumptions were removed, so the pedestrian target location and detection were completed. The experimental results on INRIA dataset show that, compared with the traditional DPM algorithm, the proposed algorithm improves the accuracy rate by 12 percentage points and significantly accelerates pedestrian detection and recognition.