Distributed visual-target-surveillance system in wireless sensor networks

  • Authors:

  • Xue Wang;Sheng Wang;Daowei Bi

  • Affiliations:

  • Institute of Instrument Science and Technology, State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, ...;State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China;State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2009

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Abstract

A wireless sensor network (WSN) is a powerful unattended distributed measurement system, which is widely used in target surveillance because of its outstanding performance in distributed sensing and signal processing. This paper introduces a multiview visual-target-surveillance system in WSN, which can autonomously implement target classification and tracking with collaborative online learning and localization. The proposed system is a hybrid system of single-node and multinode fusion. It is constructed on a peer-to-peer (P2P)-based computing paradigm and consists of some simple but feasible methods for target detection and feature extraction. Importantly, a support-vector-machine-based semisupervised learning method is used to achieve online classifier learning with only unlabeled samples. To reduce the energy consumption and increase the accuracy, a novel progressive data-fusion paradigm is proposed for online learning and localization, where a feasible routing method is adopted to implement information transmission with the tradeoff between performance and cost. Experiment results verify that the proposed surveillance system is an effective, energy-efficient, and robust system for real-world application. Furthermore, the P2P-based progressive data-fusion paradigm can improve the energy efficiency and robustness of target surveillance.