In order to overcome the problem that in the aggregate channel features（ACF） algorithm the pedestrian appearance is unstable

which resulting in a decrease in detection rate

pedestrian detection based on fast edge detection and Real Adaboost is proposed.Fast edge detection is used to detect the edge of the image. To obtain a more stable pedestrian contour information

use the edge map to replace the normalized gradient magnitude channel in the ACF algorithm.Real adaboost classifier with higher accuracy is used to improve the classification accuracy.Simulation experimental results show that compared with the original ACF algorithm

false detection rate on INRIA and Caltech database of the algorithm is decreased by 5.1 % and 14.8 % respectively.