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Prediction-Based Object Tracking in Visual Sensor Networks

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Abstract

This paper is to investigate the mobile object tracking in visual sensor networks. When visual sensors equipped with cameras are randomly deployed in a monitoring environment, many sensors are involved in covering the same mobile object. In a visual sensor network, images of the object may be captured by different sensors in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving full coverage for a set of selected characteristic points of an object invariably involves a great deal of redundant image data consuming the transmission energy for a visual sensor network. A novel approach is proposed to overcome this problem. The minimal number of sensors required for set coverage can be determined by predicting the direction and speed of the mobile object. Such sets are capable of covering the maximal number of characteristic points of view related to the mobile object at one time. The simulation results show that this approach reduces transmission cost while preserving the maximal coverage range of mobile objects.

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Acknowledgment

This work was supported in part by Ministry of Science and Technology under grants MOST-99-2221-E-024-006 and MOST-103-2221-E-262-018, Taiwan.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, Tainan, 700, Taiwan

    Tzung-Shi Chen

  2. Department of Computer Information and Network Engineering, Lunghwa University of Science and Technology, Taoyuan, 333, Taiwan

    Hua-Wen Tsai

  3. Department of Information and Learning Technology, National University of Tainan, Tainan, 700, Taiwan

    Jiun-Jie Peng

Authors
  1. Tzung-Shi Chen
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  2. Hua-Wen Tsai
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  3. Jiun-Jie Peng
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Correspondence to Tzung-Shi Chen.

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Chen, TS., Tsai, HW. & Peng, JJ. Prediction-Based Object Tracking in Visual Sensor Networks. Wireless Pers Commun 87, 145–163 (2016). https://doi.org/10.1007/s11277-015-3036-4

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