Pedestrian detection and tracking with night vision

This paper presents a method for pedestrian detection and tracking using a night vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road detection module that provides key information for pedestrian validation. Experimental comparisons have been carried out on gray-scale SVM recognition vs. binary SVM recognition and entire body detection vs. upper body detection.