Pedestrian detection is a difficult problem in the field of object detection. We combine Kobi Levi and Yair Weiss’s edge orientation histogram and Dalal’s hog(histogram of gradients) feature and apply them to pedestrian detection. We improve the algorithm from the following aspects first

we have changed the calculation formula of the original EOH (edge orientation histogram) to gain more descriptive ability. Second

we have changed the policy of updating the weight of the samples of the original Adaboost algorithm in order to reduce overfitting. Experiments show our method is very efficient. When the false positive rate is 1/10000

our detection rate is about 90% on Inria pedestrian dataset. The running speed is about 2 fps with 640×480 images on a 1.8GHz CPU.