A modular tracking system for far infrared pedestrian recognition

This paper describes a modular tracking system designed to improve the performance of a pedestrian detector. The tracking system consists of two modules, a labeler and a predictor. The former associates a tracking identifier to each pedestrian, keeping memory of the past history; this is achieved by merging the detector and predictor outputs combined with data about vehicle motion. The predictor, basically a Kalman filter, estimates the new pedestrian position by observing his previous movements. Its output helps the labeler to improve the match between the pedestrians detected in the new frame and those observed in the previous shots (feedback). If a pedestrian is occluded by some obstacle for a short while, the system continues tracking its movement using motion parameters. Moreover, it is able to reassign the same tracking ID in case the occlusion disappears in a short time. This behavior helps to correct temporary mis-recognitions that occur when the detector fails. The system has been tested using a quantitative performance evaluation tool, giving promising results.