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Journal of Sensors
Volume 2015 (2015), Article ID 760435, 12 pages
Research Article

Person Tracking System by Fusing Multicues Based on Patches

1College of Electronic Information & Control Engineering, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing 100124, China
2Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
3Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
4Department of Information Engineering and Automation, Hebei College of Industry and Technology, No. 626, Hongqi Street, Qiaoxi District, Shijiazhuang 050081, China

Received 15 January 2015; Revised 13 May 2015; Accepted 20 May 2015

Academic Editor: Yasuko Y. Maruo

Copyright © 2015 Song Min Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.