Inference of Non-Overlapping Camera Network Topology by Measuring Statistical Dependence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Self-calibration of a vision-based sensor network
Image and Vision Computing
Algebraic approach to recovering topological information in distributed camera networks
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We present a method to extract topology information from detection events of mobile entities moving through a network of binary sensors. We extract the topological structure of possible paths in the network by analyzing the time correlation of events at different sensors. The histograms of time delays between any two sensors contain the necessary information to reconstruct the network topology. This data is heavily corrupted by noise due to multiple agents in the network. We therefore use a mixture model of multiple Gaussian and a uniform distribution to explicitly isolate the noise. Our algorithm yields a graph representing the topology of our sensor network along with average travel time between nodes.