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SCOPES: Smart Cameras Object Position Estimation System

  • Ankur Kamthe
  • Lun Jiang
  • Matthew Dudys
  • Alberto Cerpa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5432)

Abstract

Wireless camera sensor networks have to balance the conflicting challenges imposed by the detection performance, latency and lifetime requirements in surveillance applications. While previous studies for camera sensor networks have addressed these issues separately, they have not quantified the trade-offs between these requirements. In this paper, we discuss the design and implementation of SCOPES, a distributed Smart Camera Object Position Estimation System that balances the trade-offs associated with camera sensor networks. The main contribution of the paper is the extensive evaluation of parameters affecting the performance of the system through analysis, simulation and experimentation in real-life conditions. Our results demonstrates the effectiveness of SCOPES, which achieves detection probabilities ranging from 84% to 98% and detection latencies from 10 seconds to 18 seconds. Moreover, by using coordination schemes, the detection performance of SCOPES was improved with increased system lifetime. SCOPES highlights that intelligent system design can compensate for resource-constrained hardware and computationally simple data processing algorithms.

Keywords

Sensor Node Detection Probability Network Lifetime Object Detection Memory Usage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2001)Google Scholar
  2. 2.
    Viola Jones Detector for CMUcam3, http://www.cmucam.org/wiki/viola-jones
  3. 3.
    Aslam, J., Butler, Z., Constantin, F., Crespi, V., Cybenko, G., Rus, D.: Tracking a moving object with a binary sensor network. In: SenSys 2003, pp. 150–161. ACM Press, New York (2003)Google Scholar
  4. 4.
    Gu, L., Jia, D., Vicaire, P., Yan, T., Luo, L., Tirumala, A., Cao, Q., He, T., Stankovic, J.A., Abdelzaher, T., Krogh, B.H.: Lightweight detection and classification for wireless sensor networks in realistic environments. In: SenSys 2005, pp. 205–217. ACM Press, New York (2005)Google Scholar
  5. 5.
    Kulkarni, P., Ganesan, D., Shenoy, P., Lu, Q.: Senseye: a multi-tier camera sensor network. In: MULTIMEDIA 2005, pp. 229–238. ACM Press, New York (2005)Google Scholar
  6. 6.
    Teixeira, T., Savvides, A.: Lightweight people counting and localizing in indoor spaces using camera sensor nodes. In: ICDSC 2007, September 25-28 (2007)Google Scholar
  7. 7.
    Jung, D., Teixeira, T., Barton-Sweeney, A., Savvides, A.: Model-based design exploration of wireless sensor node lifetimes. In: Langendoen, K.G., Voigt, T. (eds.) EWSN 2007. LNCS, vol. 4373, pp. 277–292. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Rahimi, M., Baer, R., Iroezi, O.I., Garcia, J.C., Warrior, J., Estrin, D., Srivastava, M.: Cyclops: in situ image sensing and interpretation in wireless sensor networks. In: SenSys 2005, pp. 192–204. ACM Press, New York (2005)Google Scholar
  9. 9.
  10. 10.
    Ji, P., Ge, Z., Kurose, J., Towsley, D.: A comparison of hard-state and soft-state signaling protocols. IEEE/ACM Transactions on Networking 15(2), 281–294 (2007)CrossRefGoogle Scholar
  11. 11.
    NationMaster Orders of magnitude (speed), http://www.nationmaster.com/encyclopedia/

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ankur Kamthe
    • 1
  • Lun Jiang
    • 1
  • Matthew Dudys
    • 1
  • Alberto Cerpa
    • 1
  1. 1.Electrical Engineering and Computer ScienceUniversity of CaliforniaMercedUSA

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