Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Track Objects Through Unobserved Regions
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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
Activity Topology Estimation for Large Networks of Cameras
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
A stochastic approach to tracking objects across multiple cameras
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Searching in space and time: a system for forensic analysis of large video repositories
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
A learning approach to interactive browsing of surveillance content
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Modeling Coverage in Camera Networks: A Survey
International Journal of Computer Vision
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Recent research on video surveillance across multiple cameras has typically focused on camera networks of the order of 10 cameras. In this paper we argue that existing systems do not scale to a network of hundreds, or thousands, of cameras. We describe the design and deployment of an algorithm called exclusion that is specifically aimed at finding correspondence between regions in cameras for large camera networks. The information recovered by exclusion can be used as the basis for other surveillance tasks such as tracking people through the network, or as an aid to human inspection. We have run this algorithm on a campus network of over 100 cameras, and report on its performance and accuracy over this network.