Algebraic approach to recovering topological information in distributed camera networks
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Topology Inference in Wireless Mesh Networks
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Distributed tracking in a large-scale network of smart cameras
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
A distributed topological camera network representation for tracking applications
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Modeling Coverage in Camera Networks: A Survey
International Journal of Computer Vision
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In this paper we describe a technique to infer the topology and connectivity information of a network of cameras based on observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data and verified with experiments conducted on a six camera sensor network.