Abstract Smart cameras combine video sensing, video processing and communication onto a single embedded device. Due to their high onboard processing and communication power they are able to perform sophisticated video tasks in real time and can, therefore, react to changes in the camera’s environment. Such embedded smart cameras are excellent candidates for reactive sensor systems. In this paper we describe the necessary hard- and software components in order to perform high level video processing onboard. We then present a multi-camera setup which is able to autonomously react to significant changes in the observed environment. We further present ongoing research in extending our smart camera platform towards an autonomous distributed smart sensor system.

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. Copyright © 2007 Markus Quaritsch et al. 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. 1.