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Distributed Sensing and Processing for Multi-Camera Networks

  • Chapter
Distributed Video Sensor Networks

Abstract

Sensor networks with large numbers of cameras are becoming increasingly prevalent in a wide range of applications, including video conferencing, motion capture, surveillance, and clinical diagnostics. In this chapter, we identify some of the fundamental challenges in designing such systems: robust statistical inference, computationally efficiency, and opportunistic and parsimonious sensing. We show that the geometric constraints induced by the imaging process are extremely useful for identifying and designing optimal estimators for object detection and tracking tasks. We also derive pipelined and parallelized implementations of popular tools used for statistical inference in non-linear systems, of which multi-camera systems are examples. Finally, we highlight the use of the emerging theory of compressive sensing in reducing the amount of data sensed and communicated by a camera network.

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Acknowledgements

This research was partially supported by the Office of Naval Research under the contracts N00014-09-1-1162 and N00014-07-1-0936, the U.S. Army Research Laboratory and the U.S. Army Research Office under grant number W911NF-09-1-0383, and the AFOSR under the contracts FA9550-09-1-0432 and FA9550-07-1-0301. The authors thank Prof. Volkan Cevher, Prof. Ankur Srivastava, Dr. Ashok Veeraraghavan, Dr. Marco Duarte and Mr. Dikpal Reddy for valuable discussions and collaborations.

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Authors and Affiliations

    Authors
    1. Aswin C. Sankaranarayanan
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    2. Rama Chellappa
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    3. Richard G. Baraniuk
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    Corresponding author

    Correspondence to Aswin C. Sankaranarayanan .

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    Editors and Affiliations

    1. Center for Research, Intelligent Systems, University of California, Riverside, Riverside, 92521, California, USA

      Bir Bhanu

    2. Dept. Computer Science & Engineering, University of California, Riverside, Riverside, 92521, California, USA

      Chinya V. Ravishankar

    3. Dept. Electrical Engineering, University of California, Riverside, Riverside, 92521, California, USA

      Amit K. Roy-Chowdhury

    4. Dept. Electrical Engineering, Packard Bldg., Stanford University, Serra Mall 350, Stanford, 94305-9505, California, USA

      Hamid Aghajan

    5. Dept. Computer Science, University of California, Los Angeles, Boelter Hall 4731, Los Angeles, 90095-1596, California, USA

      Demetri Terzopoulos

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    © 2011 Springer-Verlag London Limited

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    Sankaranarayanan, A.C., Chellappa, R., Baraniuk, R.G. (2011). Distributed Sensing and Processing for Multi-Camera Networks. In: Bhanu, B., Ravishankar, C., Roy-Chowdhury, A., Aghajan, H., Terzopoulos, D. (eds) Distributed Video Sensor Networks. Springer, London. https://doi.org/10.1007/978-0-85729-127-1_6

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    • DOI: https://doi.org/10.1007/978-0-85729-127-1_6

    • Publisher Name: Springer, London

    • Print ISBN: 978-0-85729-126-4

    • Online ISBN: 978-0-85729-127-1

    • eBook Packages: Computer ScienceComputer Science (R0)

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