Promising directions in active vision
International Journal of Computer Vision
Consistency management in Deno
Mobile Networks and Applications
Distributed sensor network for real time tracking
Proceedings of the fifth international conference on Autonomous agents
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Active Image Capturing and Dynamic Scene Visualization by Cooperative Distributed Vision
AMCP '98 Proceedings of the First International Conference on Advanced Multimedia Content Processing
Organic Computing-Vision and Challenge for System Design
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
Active-Vision for the Autonomous Surveillance of Dynamic, Multi-Object Environments
Journal of Intelligent and Robotic Systems
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Vision systems are camera networks, which perform computer vision using multiple cameras. Due to the directional characteristic of the image sensor, it is not feasible to observe large surveillance areas using stationary cameras. This drawback can be overcome by using active vision (AcVi) systems consisting of mobile cameras (called AcVi nodes), which are reconfigurable in both position and orientation. In case of a dynamic environment with moving targets, AcVi nodes can be repositioned during runtime in order to fulfill the overall observation objectives. This paper is devoted to develop a generalized system architecture for AcVi systems in multi-target environments. Based on this architecture, we present a coordination mechanism making way for self-organization - a major paradigm of Organic Computing - in such systems. Our algorithm enables AcVi nodes to observe an area under surveillance by using their mobility. In contrast to stationary vision systems, only a fraction of the amount of nodes is needed to achieve the same observation quality. The algorithm has been evaluated by simulation with up to 10 nodes as used for example for surveillance scenarios. Results show that our coordination algorithm is able to cope with large numbers of nodes and targets and is robust towards real world disturbances like communication failure.