Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Automatic Learning of an Activity-Based Semantic Scene Model
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
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
Finding camera overlap in large surveillance networks
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A stochastic approach to tracking objects across multiple cameras
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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In this paper, we present a novel application for interactive browsing of (recorded) surveillance content. The application is based on user feedback and enables an operator to switch between camera views that are likely to contain the same activity. Our system relies on off-the-shelf background-subtraction activity detection mechanisms. We use two techniques from machine learning to automatically learn the topology of surveillance camera networks. The first technique identifies connections between camera views for which objects are temporarily out of view, while the second technique identifies overlap between views. Testing on an actual surveillance camera network suggests that the approach is both accurate and robust, despite the simplicity of the involved computer vision methods.