A Cooperative Object Tracking System with Fuzzy-Based Adaptive Camera Selection

The intelligent environments, built upon many distributed sensors, are promising technology for ubiquitous interaction between robots and human beings. Especially, it is important to track target objects and get the positional information of them in such environments. This paper focuses on adapting camera selection for target tracking in multi-camera system. In this paper, a fuzzy automaton based camera selection method is introduced. In the proposed method, the camera selection decision is driven by fuzzy automaton based on the previously selected camera (previous camera state) and the tracking level of the object in each available camera. Simulations for evaluation of the proposed method and comparison with the previous method are presented. The results show that the proposed method is efficient for adaptive camera selection in multi-camera environment and helps easy construction of multi-camera placement. An actual multi-camera system with the proposed camera selection method was developed for checking tracking performance in the real environment. Experiments in the constructed system show that the proposed method suits well the camera selection task for tracking a moving object in the real intelligent environment.

[1]  Joo-Ho Lee,et al.  Intelligent Space — concept and contents , 2002, Adv. Robotics.

[2]  Takashi Suehiro,et al.  RT-middleware: distributed component middleware for RT (robot technology) , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  J.-H. Lee,et al.  Fuzzy-based camera selection for object tracking in a multi-camera system , 2008, 2008 Conference on Human System Interactions.

[4]  L. T. Kóczy,et al.  Behaviour based techniques in user adaptive Kansei technology , 2003 .

[5]  Ruzena Bajcsy,et al.  The sensor selection problem for bounded uncertainty sensing models , 2005 .

[6]  Karl F. MacDorman,et al.  A memory-based distributed vision system that employs a form of attention to recognize group activity at a subway station , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[7]  Liang Liu,et al.  Optimal camera selection for target localization in camera sensor networks , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[8]  S. Kovics Fuzzy reasoning and fuzzy automata in user adaptive emotional and information retrieval systems , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[9]  Hideki Hashimoto,et al.  Adaptive Personalisation of the Intelligent Space by Fuzzy Automaton Szilveszter Kovács , 2006 .

[10]  Hideki Hashimoto,et al.  Human-following mobile robot in a distributed intelligent sensor network , 2004, IEEE Transactions on Industrial Electronics.

[11]  S. Kovács Interpolation-based Fuzzy Reasoning as an Application Oriented Approach , 2005 .

[12]  Leonidas J. Guibas,et al.  Sensor Tasking for Occupancy Reasoning in a Network of Cameras , 2004 .

[13]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Mohan M. Trivedi,et al.  Video arrays for real-time tracking of person, head, and face in an intelligent room , 2003, Machine Vision and Applications.

[15]  Szilveszter Kovács,et al.  Extending the Fuzzy Rule Interpolation "FIVE" by Fuzzy Observation , 2006 .

[16]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[17]  Szilveszter Kovács,et al.  Interpolative Fuzzy Reasoning in Behaviour-Based Control , 2004, Fuzzy Days.

[18]  T. Sato,et al.  Environment-type robot system "RoboticRoom" featured by behavior media, behavior contents, and behavior adaptation , 2004, IEEE/ASME Transactions on Mechatronics.

[19]  N. Ando,et al.  Cooperation of distributed intelligent sensors in intelligent environment , 2004, IEEE/ASME Transactions on Mechatronics.

[20]  Ruzena Bajcsy,et al.  The Sensor Selection Problem for Bounded Uncertainty Sensing Models , 2005, IEEE Transactions on Automation Science and Engineering.