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Tracking in Urban Environments Using Sensor Networks Based on Audio-Video Fusion

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Abstract

Heterogeneous sensor networks (HSNs) are gaining popularity in diverse fields, such as military surveillance, equipment monitoring, and target tracking yarvis:2005:infocom. They are natural steps in the evolution of wireless sensor networks wireless sensor network (WSNs) driven by several factors. Increasingly, WSNs will need to support multiple, although not necessarily concurrent, applications. Different applications may require different resources. Some applications can make use of nodes with different capabilities. As the technology matures, new types of nodes will become available and existing deployments will be refreshed. Diverse nodes will need to coexist and support old and new applications.

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References

  1. A. M. Ali, K. Yao, T. C. Collier, C. E. Taylor, D. T. Blumstein, and L. Girod. An empirical study of collaborative acoustic source localization. In IPSN ’07: Proceedings of the 6th international conference on Information processing in sensor networks, pages 41–50, 2007.

    Google Scholar 

  2. I. Amundson, B. Kusy, P. Volgyesi, X. Koutsoukos, and A. Ledeczi. Time synchronization in heterogeneous sensor networks. In International Conference on Distributed Computing in Sensor Networks (DCOSS 2008), 2008.

    Google Scholar 

  3. M. J. Beal, N. Jojic, and H. Attias. A graphical model for audiovisual object tracking. volume 25, pages 828–836, 2003.

    Google Scholar 

  4. P. Bergamo, S. Asgari, H. Wang, D. Maniezzo, L. Yip, R. E. Hudson, K. Yao, and D. Estrin. Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberence. In IEEE Transactions On Mobile Computing, volume 3, pages 211–224, 2004.

    Article  Google Scholar 

  5. S. T. Birchfield. A unifying framework for acoustic localization. In Proceedings of the 12th European Signal Processing Conference (EUSIPCO), Vienna, Austria, September 2004.

    Google Scholar 

  6. N. Checka, K. Wilson, V. Rangarajan, and T. Darrell. A probabilistic framework for multi-modal multi-person tracking. In IEEE Workshop on Multi-Object Tracking, 2003.

    Google Scholar 

  7. J. C. Chen, K. Yao, and R. E. Hudson. Acoustic source localization and beamforming: theory and practice. In EURASIP Journal on Applied Signal Processing, pages 359–370, April 2003.

    Google Scholar 

  8. T. Darrell, D. Demirdjian, N. Checka, and P. Felzenszwalb. Plan-view trajectory estimation with dense stereo background models. In Proceedings. Eighth IEEE International Conference on Computer Vision (ICCV 2001), 2001.

    Google Scholar 

  9. M. Ding, A. Terzis, I.-J. Wang, and D. Lucarelli. Multi-modal calibration of surveillance sensor networks. In Military Communications Conference, MILCOM 2006, 2006.

    Google Scholar 

  10. E. Duarte-Melo and M. Liu. Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks. In IEEE Globecom, 2002.

    Google Scholar 

  11. A. Elgammal, D. Harwood, and L. Davis. Non-parametric model for background subtraction. In IEEE ICCV’99 Frame-Rate workshop, 1999.

    Google Scholar 

  12. J. Elson, L. Girod, and D. Estrin. Fine-grained network time synchronization using reference broadcasts. In Operating Systems Design and Implementation (OSDI), 2002.

    Google Scholar 

  13. N. Friedman and S. Russell. Image segmentation in video sequences: A probabilistic approach. In Conference on Uncertainty in Artificial Intelligence, 1997.

    Google Scholar 

  14. S. Ganeriwal, R. Kumar, and M. B. Srivastava. Timing-sync protocol for sensor networks. In ACM SenSys, 2003.

    Google Scholar 

  15. L. Girod, V. Bychkovsky, J. Elson, and D. Estrin. Locating tiny sensors in time and space: A case study. In ICCD, 2002.

    Google Scholar 

  16. L. Girod, M. Lukac, V. Trifa, and D. Estrin. The design and implementation of a self-calibrating distributed acoustic sensing platform. In SenSys ’06, 2006.

    Google Scholar 

  17. D. Hall and J. Llinas. An introduction to multisensor data fusion. In Proceedings of the IEEE, volume 85, pages 6–23, 1997.

    Article  Google Scholar 

  18. H. Y. Hau and R. L. Kashyap. On the robustness of Dempster’s rule of combination. In IEEE International Workshop on Tools for Artificial Intelligence, 1989.

    Google Scholar 

  19. P. KaewTraKulPong and R. B. Jeremy. An improved adaptive background mixture model for realtime tracking with shadow detection. In Workshop on Advanced Video Based Surveillance Systems (AVBS), 2001.

    Google Scholar 

  20. Z. Khan, T. Balch, and F. Dellaert. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11):1805–1918, Nov. 2005.

    Article  Google Scholar 

  21. D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell. Towards robust automatic traffic scene analysis in realtime. In IEEE Conference on Decision and Control, 1994.

    Google Scholar 

  22. R. Kumar, V. Tsiatsis, and M. B. Srivastava. Computation hierarchy for in-network processing. 2003.

    Google Scholar 

  23. B. Kusy, P. Dutta, P. Levis, M. Maroti, A. Ledeczi, and D. Culler. Elapsed time on arrival: A simple and versatile primitive for time synchronization services. International Journal of Ad hoc and Ubiquitous Computing, 2(1), January 2006.

    Google Scholar 

  24. A. Ledeczi, A. Nadas, P. Volgyesi, G. Balogh, B. Kusy, J. Sallai, G. Pap, S. Dora, K. Molnar, M. Maroti, and G. Simon. Countersniper system for urban warfare. ACM Trans. Sensor Networks, 1(2), 2005.

    Google Scholar 

  25. P. Levis, S. Madden, D. Gay, J. Polastre, R. Szewczyk, A. Woo, E. Brewer, and D. Culler. The emergence of networking abstractions and techniques in TinyOS. In NSDI, 2004.

    Google Scholar 

  26. J. Liu, J. Reich, and F. Zhao. Collaborative in-network processing for target tracking. In EURASIP, Journal on Applied Signal Processing, 2002.

    Google Scholar 

  27. M. Maroti, B. Kusy, G. Simon, and A. Ledeczi. The flooding time synchronization protocol. In ACM SenSys, 2004.

    Google Scholar 

  28. M. Meingast, M. Kushwaha, S. Oh, X. Koutsoukos, and S. S. Akos Ledeczi. Heterogeneous camera network localization using data fusion. In In ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC-08), 2008.

    Google Scholar 

  29. S. Oh, S. Russell, and S. Sastry. Markov chain Monte Carlo data association for general multiple-target tracking. In IEEE Transactions on Automatic Control, 54(3):481–497, Mar. 2009.

    Article  MathSciNet  Google Scholar 

  30. K. Okuma, A. Taleghani, N. de Freitas, J. Little, and D. Lowe. A boosted particle filter: Multitarget detection and tracking. In European Conference on Computer Vision, 2004.

    Google Scholar 

  31. J. Piater and J. Crowley. Multi-modal tracking of interacting targets using Gaussian approximations. In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2001.

    Google Scholar 

  32. A. Poore. Multidimensional assignment and multitarget tracking. In I. J. Cox, P. Hansen, and B. Julesz, editors, Partitioning Data Sets, pages 169–196. American Mathematical Society, 1995.

    Google Scholar 

  33. D. Reid. An algorithm for tracking multiple targets. IEEE Trans. Automatic Control, 24(6):843–854, December 1979.

    Article  Google Scholar 

  34. S. Rhee, D. Seetharam, and S. Liu. Techniques for minimizing power consumption in low data-rate wireless sensor networks. 2004.

    Google Scholar 

  35. J. Sallai, B. Kusy, A. Ledeczi, and P. Dutta. On the scalability of routing integrated time synchronization. In Workshop on Wireless Sensor Networks (EWSN), 2006.

    Google Scholar 

  36. C. Stauffer and W. Grimson. Learning patterns of activity using real-time tracking. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.

    Google Scholar 

  37. N. Strobel, S. Spors, and R. Rabenstein. Joint audio video object localization and tracking. In IEEE Signal Processing Magazine, 2001.

    Google Scholar 

  38. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In IEEE International Conference on Computer Vision, 1999.

    Google Scholar 

  39. J. Valin, F. Michaud, and J. Rouat. Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering. In Robot. Auton. Syst., volume 55, pages 216–228, March 2007.

    Article  Google Scholar 

  40. P. Volgyesi, G. Balogh, A. Nadas, C. Nash, and A. Ledeczi. Shooter localization and weapon classification with soldier-wearable networked sensors. In Mobisys, 2007.

    Google Scholar 

  41. M. Yarvis, N. Kushalnagar, H. Singh, A. Rangarajan, Y. Liu, and S. Singh. Exploiting heterogeneity in sensor networks. In IEEE INFOCOM, 2005.

    Google Scholar 

  42. B. H. Yoshimi and G. S. Pingali. A multimodal speaker detection and tracking system for teleconferencing. In ACM Multimedia ’02, 2002.

    Google Scholar 

  43. T. Zhao and R. Nevatia. Tracking multiple humans in crowded environment. In IEEE Conference on Computer Vision and Pattern Recognition, 2004.

    Google Scholar 

  44. D. Zotkin, R. Duraiswami, H. Nanda, and L. S. Davis. Multimodal tracking for smart videoconferencing. In In Proc. 2nd Int. Conference on Multimedia and Expo (ICME’01), 2001.

    Google Scholar 

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Correspondence to Manish Kushwaha , Songhwai Oh or Isaac Amundson .

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Kushwaha, M., Oh, S., Amundson, I., Koutsoukos, X., Ledeczi, A. (2010). Tracking in Urban Environments Using Sensor Networks Based on Audio-Video Fusion. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds) Handbook of Ambient Intelligence and Smart Environments. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-93808-0_5

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  • DOI: https://doi.org/10.1007/978-0-387-93808-0_5

  • Publisher Name: Springer, Boston, MA

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  • Online ISBN: 978-0-387-93808-0

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