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Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10746))

Abstract

Multi-camera object tracking is an efficient approach commonly used in security and surveillance systems. In a conventional multi-camera setup, a central computational unit processes large amounts of data in real time that is provided by distributed cameras. High network traffic, cost of storage on the central unit, scalability of the system, and vulnerability of the central unit to attacks are among the disadvantages of such systems. In this paper, we present an autonomous multi-camera tracking system to overcome these challenges. We assume cameras that are capable of limited computation for locally tracking a subset of objects in the scene, as well as peer-to-peer network connectivity among the cameras with a decent bandwidth that is sufficient for message passing to achieve coordination. We propose an efficient distributed algorithm for coordination and load-balancing among the cameras. We also provide experimental results to validate the utility of the proposed algorithm in comparison to a centralized algorithm.

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Authors and Affiliations

  1. Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, USA

    Yusuf Osmanlıoğlu

  2. Department of Computer Science, Drexel University, Philadelphia, USA

    Bahareh Shakibajahromi & Ali Shokoufandeh

Authors
  1. Yusuf Osmanlıoğlu
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  2. Bahareh Shakibajahromi
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  3. Ali Shokoufandeh
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Corresponding author

Correspondence to Yusuf Osmanlıoğlu .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. University of York, York, United Kingdom

    Edwin Hancock

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Osmanlıoğlu, Y., Shakibajahromi, B., Shokoufandeh, A. (2018). Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization. In: Pelillo, M., Hancock, E. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science(), vol 10746. Springer, Cham. https://doi.org/10.1007/978-3-319-78199-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-78199-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78198-3

  • Online ISBN: 978-3-319-78199-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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