Fast pedestrain detection with cascade classifiers

In this paper, we propose a method for fast pedestrian detection in images/videos. Multi-scale orientated (MSO) features are proposed to represent coarse pedestrian contour, on which Adaboost classifiers are trained for pedestrian coarse location. In the fine detection, histogram of oriented gradient (HOG) features and SVM classifiers are employed to precisely classify pedestrians and non-pedestrians. The coarse-to-fine scheme can bring out not only a higher speed but also the elimination of smooth image regions that are prone to be falsely detection as positives by strong classifiers. The strong classifier SVM in the fine detection make the detection robust to variance of pedestrian pattern. Experiments validates the proposed method.