Detection of Multiple Pedestrians using Motion Information and Adaboost Algorithm

Robust detection of pedestrians in video streams is important for many applications. However, if pedestrians are adjacent to each other, it is much more difficult to accurately detect them. In this paper, we propose a method to detect multiple pedestrians using motion information and Adaboost algorithm from an image sequence acquired by a single camera on a mobile or stationary system. In case of mobile system, the ego-motion of the camera is compensated for using corresponding feature sets. The region of interest is calculated by the difference image between two consecutive images using the compensated image. Pedestrian detector is learned by boosting a number of weak classifiers which are based on Histogram of Oriented Gradient (HOG) features. The proposed approach has been tested to a number of image sequences, and it was shown to detect multiple pedestrians very well.