Pedestrian detection is a highspot and challenge research work in the area of computer vision and pattern recognition. The aggregate channel feature (ACF) algorithm generates lower detecting precision and higher log-average miss rate(LAMR) for pedestrian detection. We proposed an improve pedestrian detection method based on ACF algorithm in this paper. Firstly, we introduce objectness method to further verify low detection score object area captured by ACF, which can reduce false positive (FP) of the algorithm to some degree. Then, we combine the score with location of the detection window to modify the non-maximum suppression (Nms) algorithm, and the AP increases by 0.41%, while the LAMR decreases by 1.49%. Finally, we implement cascading detection for detection area by using a given threshold score and a casDPM model. The AP increases by 0.65%, and the LAMR decreases by 2.06%. Experiments on INRIA dataset are conducted and validated, and the results show that our approach not only meets the needs of real-time detection, but also obviously decreases FP, and displays a good detection effect.