KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Compressed Sensing-based Multiple-target Tracking Algorithm for Ad Hoc Camera Sensor Networks

  • Lu, Xu (School of Automatization, Guangdong Polytechnic Normal University) ;
  • Cheng, Lianglun (School of Automatization, Guangdong University of Technology) ;
  • Liu, Jun (School of Automatization, Guangdong Polytechnic Normal University) ;
  • Chen, Rongjun (School of Computer Science, Guangdong Polytechnic Normal University)
  • Received : 2017.04.21
  • Accepted : 2017.11.06
  • Published : 2018.03.31
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Abstract

Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.

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References

  1. C. Cordeiro, D. P. Agrawal, "Wireless sensor networks," Communications of the Acm, vol. 47, no. 6, pp.30-33, 2004. https://doi.org/10.1145/1029496.1029522
  2. X. Liu. "A survey on wireless camera sensor networks," Frontier and Future Development of Information Technology in Medicine and Education, pp.1085-1094, 2014.
  3. V. Tran-Quang, T. Ngo-Quynh and M. Jo, "A lateration-localizing algorithm for energy-efficient target tracking in wireless sensor networks," Ad Hoc & Sensor Wireless Networks, vol. 34, no.1-4, pp.191-220, 2016.
  4. Y. Wang, D. Wang and W Fang, "Automatic node selection and target tracking in wireless camera sensor networks," Computers & Electrical Engineering, vol. 40, no. 2, pp.484-493, 2014. https://doi.org/10.1016/j.compeleceng.2013.07.005
  5. A. D. S. Bernabe, "Efficient cluster-based tracking mechanisms for camera-based wireless sensor networks," IEEE Transactions on Mobile Computing, vol. 14, no. 9, pp. 1820-1832, 2015. https://doi.org/10.1109/TMC.2014.2374164
  6. N. Tezcan, W. Wang, "Self-orienting wireless multimedia sensor networks for occlusion-free viewpoints," Computer Networks, vol. 52, no. 13, pp.2558-2567, 2008. https://doi.org/10.1016/j.comnet.2008.05.014
  7. M. Alaei, J. M. Barcelo-Ordinas, "A collaborative node management scheme for energy-efficient monitoring in wireless multimedia sensor networks," Wireless Networks, vol. 19, no. 19, pp.639-659, 2013. https://doi.org/10.1007/s11276-012-0492-6
  8. A. O. Ercan, D. B. Yang, A. E. Gamal, L. J. Guibas, "Optimal placement and selection of camera network nodes for target localization," Distributed Computing in Sensor Systems, pp.389-404, 2006.
  9. W. Shaw, Y. He and I. Lee, "Mobile sink to track multiple targets in wireless visual sensor networks," in Proc. of International Symposium on Ubiquitous Multimedia Computing, pp.51-56, 2008.
  10. E. J. Candes. "Compressive sampling," Marta Sanz Sole, vol. 17, no. 2, pp. 1433-1452, 2006.
  11. E. J. Candes, M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp.21-30, 2008. https://doi.org/10.1109/MSP.2007.914731
  12. S. Li, L. D. Xu, X. Wang, "Compressed sensing signal and data acquisition in wireless sensor networks and Internet of Things," IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp.2177-2186, 2013. https://doi.org/10.1109/TII.2012.2189222
  13. X. Yang, X. Tao, E. Dutkiewicz, X. Huang, Y. J. Guo, Q. Cui, "Energy-efficient distributed data storage for wireless sensor networks based on compressed sensing and network coding," IEEE Transactions on Wireless Communications, vol. 12, no.12, pp.5087-5099, 2013. https://doi.org/10.1109/TWC.2013.090313.121804
  14. F. H. He, Z. J. Yu, H. T. Liu, "Multiple target localization via compressed sensing in wireless sensor networks," Journal of Electronics & Information Technology, vol. 34, no. 3, pp.716-721, 2012.
  15. Y. J. Zheng, W. Thakshila, K. V. Pramod, "Probabilistic sensor management for target tracking via compressive sensing," in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, pp.5075-5079, 2014.
  16. C. Feng, S. Valaee, Z. H. Tan, "Multiple target localization using compressive sensing," in Proc. of IEEE Global Communications Conference, pp.1-6, 2009.
  17. L. Liu, X. Zhang, H. Ma, "Localization-oriented coverage in wireless camera sensor networks," IEEE Transactions on Wireless Communications, vol. 10, no. 2, pp.484-494, 2011. https://doi.org/10.1109/TWC.2010.01.080956
  18. L. C. Jiao, S. Y. Yang, "Development and prospect of compressive sensing," Journal of Electronic, vol. 39, no. 7, pp.1651-1662, 2011.
  19. D. L. Donnho, Y. Tsaig, "Extensions of compressed sensing," Signal Processing, vol. 86, no. 3, pp.533-548, 2006. https://doi.org/10.1016/j.sigpro.2005.05.028
  20. E. J. Candes, "The restricted isometry property and its implications for compressed sensing," Comptes Rendus Mathematique, vol. 346, no. 9, pp.589-592, 2008. https://doi.org/10.1016/j.crma.2008.03.014
  21. Candes E J, Y. Plan, "A probabilistic and RIPless theory of compressed sensing," IEEE Transactions on Information Theory, vol. 57, no. 11, pp.7235-7254, 2011. https://doi.org/10.1109/TIT.2011.2161794
  22. M. L. Kaddachi, L. Makkaoui, A. Soudani, V. Lecuire,, J. M. Moureaux, "FPGA-based image compression for low-power wireless camera sensor networks," in Proc. of International Conference on Next Generation Networks & Services, pp.68-71, 2011.