Person Tracking System by Fusing Multicues Based on Patches

Abstract

A person tracking algorithm by fusing multicues based on patches is proposed to solve the problem of distinguishing person, occlusion, and illumination variations. Kinect is mounted on the robot for providing color images and depth maps. A detector representing a person by using the fusion of multicues based on patches is proposed. The detector divides the person into many patches and then represents each patch by using depth-color histograms and depth-texture histograms. The appearance representation, considering depth, color, and texture information, has powerful discrimination ability to handle the problems of occlusion, illumination changes, and pose variations. Considering the motion of the robot and person, a tracker called motion extended Kalman filter (MEKF) is presented to predict the person’s position. The result of the tracker is treated as a candidate sample of the detector, and then the result of the detector is the previous knowledge of the tracker. The detector and tracker supplement each other and improve the tracking performance. To drive the robot towards the given person precisely, a fuzzy based intelligent gear control strategy (FZ-IGS) is implemented. Experiments demonstrate that the proposed approach can track a person in a complex environment and have an optimum performance.