Self-Organization of Randomly Placed Sensors
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computer Networks: A Systems Approach, 3rd Edition
Computer Networks: A Systems Approach, 3rd Edition
Learning Models for Predicting Recognition Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
Recovering Non-overlapping Network Topology Using Far-field Vehicle Tracking Data
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Super-resolution and facial expression for face recognition in video
Super-resolution and facial expression for face recognition in video
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Integrating Face and Gait for Human Recognition at a Distance in Video
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
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A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.