Socio-economic vision graph generation and handover in distributed smart camera networks

  • Authors:

  • Lukas Esterle;Peter R. Lewis;Xin Yao;Bernhard Rinner

  • Affiliations:

  • Alpen-Adria Universit&atuml/ Klagenfurt and Lakeside Labs, AT, Klagenfurt, Austria;University of Birmingham, UK;University of Birmingham, UK;Alpen-Adria Universit&atuml/ Klagenfurt and Lakeside Labs, AT, Klagenfurt, Austria

  • Venue:
  • ACM Transactions on Sensor Networks (TOSN)
  • Year:
  • 2014

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Abstract

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.