Open Access

Autonomous Multicamera Tracking on Embedded Smart Cameras

  • Markus Quaritsch1Email author,
  • Markus Kreuzthaler1,
  • Bernhard Rinner1,
  • Horst Bischof2 and
  • Bernhard Strobl3
EURASIP Journal on Embedded Systems20072007:092827

DOI: 10.1155/2007/92827

Received: 28 April 2006

Accepted: 30 October 2006

Published: 24 January 2007


There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power. In this paper we present a multicamera tracking method on distributed, embedded smart cameras. Smart cameras combine video sensing, processing, and communication on a single embedded device which is equipped with a multiprocessor computation and communication infrastructure. Our multicamera tracking approach focuses on a fully decentralized handover procedure between adjacent cameras. The basic idea is to initiate a single tracking instance in the multicamera system for each object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observes the object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fully implemented this novel multicamera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. Our approach has been successfully evaluated on tracking persons at our campus.

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Authors’ Affiliations

Institute for Technical Informatics, Graz University of Technology
Institute for Computer Graphics and Vision, Graz University of Technology
Video and Safety Technology, Austrian Research Centers GmbH


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© Markus Quaritsch et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.