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Efficient network clustering for traffic reduction in embedded smart camera networks

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Abstract

In this work, a clustering approach for bandwidth reduction in distributed smart camera networks is presented. Properties of the environment such as camera positions and environment pathways, as well as dynamics and features of targets are used to limit the flood of messages in the network. To better understand the correlation between camera positioning and pathways in the scene on one hand and temporal and spatial properties of targets on the other hand, and to devise a sound messaging infrastructure, a unifying probabilistic modeling for object association across multiple cameras with disjointed view is used. Communication is efficiently handled using a task-oriented node clustering that partition the network in different groups according to the pathway among cameras, and the appearance and temporal behavior of targets. We propose a novel asynchronous event exchange strategy to handle sporadic messages generated by non-frequent tasks in a distributed tracking application. Using a Xilinx-FPGA with embedded Microblaze processor, we could show that, with limited resource and speed, the embedded processor was able to sustain a high communication load, while performing complex image processing computations.

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Authors and Affiliations

  1. Dspace GmbH, Paderborn, Germany

    Ali Akbar Zarezadeh

  2. CSCE Department, University of Arkansas, Fayetteville, USA

    Christophe Bobda, Franck Yonga & Michael Mefenza

Authors
  1. Ali Akbar Zarezadeh
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  2. Christophe Bobda
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  3. Franck Yonga
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  4. Michael Mefenza
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Correspondence to Christophe Bobda.

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Zarezadeh, A.A., Bobda, C., Yonga, F. et al. Efficient network clustering for traffic reduction in embedded smart camera networks. J Real-Time Image Proc 12, 813–826 (2016). https://doi.org/10.1007/s11554-015-0498-2

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  • DOI: https://doi.org/10.1007/s11554-015-0498-2

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