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Distributed visual-target-surveillance system in wireless sensor networks.

Xue Wang¹, Sheng Wang, Daowei Bi

¹Department of Precision Instruments and Mechanology, Institute of Instrument Science and Technology, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China. wangxue@mail.tsinghua.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 2, 2009

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This study presents a multiview visual surveillance system using wireless sensor networks (WSNs) for autonomous target tracking and classification. The system enhances energy efficiency and robustness through collaborative online learning and a progressive data-fusion paradigm.

Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Wireless sensor networks (WSNs) offer robust distributed sensing and signal processing capabilities.
Target surveillance systems require autonomous operation for classification and tracking.
Energy efficiency and accuracy are critical challenges in WSN-based surveillance.

Purpose of the Study:

To introduce a multiview visual-target-surveillance system within a WSN framework.
To enable autonomous target classification and tracking using collaborative online learning and localization.
To enhance system performance through a novel progressive data-fusion paradigm.

Main Methods:

A hybrid system combining single-node and multinode fusion on a peer-to-peer (P2P) computing paradigm.

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Utilizing simple methods for target detection and feature extraction.

Implementing a support-vector-machine-based semisupervised learning approach for online classifier training with unlabeled data.

Adopting a progressive data-fusion paradigm with a feasible routing method for energy-efficient information transmission.

Main Results:

The proposed system demonstrates autonomous target classification and tracking.
The semisupervised learning method effectively trains classifiers using unlabeled samples.
The progressive data-fusion paradigm optimizes the tradeoff between performance and cost for information transmission.
Experimental results validate the system's effectiveness, energy efficiency, and robustness.

Conclusions:

The developed multiview visual-target-surveillance system is effective for real-world applications.
The P2P-based progressive data-fusion paradigm significantly improves energy efficiency and robustness in target surveillance.
The system offers a viable solution for autonomous, collaborative, and energy-conscious target monitoring.