Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm

Abstract

Robust detection and tracking of pedestrians in image sequences are essential for many vision applications. In this paper, we propose a method to detect and track multiple pedestrians using motion, color information and the AdaBoost algorithm. Our approach detects pedestrians in a walking pose from a single camera on a mobile or stationary system. In the case of mobile systems, ego-motion of the camera is compensated for by 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. Pedestrians are tracked by block matching method using color information. Our tracking system can track pedestrians with possibly partial occlusions and without misses using information stored in advance even after occlusion is ended. The proposed approach has been tested on a number of image sequences, and was shown to detect and track multiple pedestrians very well.