CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Online Multicamera Tracking with a Switching State-Space Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Non-overlapping Distributed Tracking using Particle Filter
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multi-view-based Cooperative Tracking of Multiple Human Objects in Cluttered Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
EURASIP Journal on Applied Signal Processing
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
Multi-camera people tracking by collaborative particle filters and principal axis-based integration
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Continuously tracking objects across multiple widely separated cameras
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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A large network of cameras is necessary for covering large areas in surveillance applications. In such systems, gaps between the fields of view of different cameras are often unavoidable. We present a method for path recovery of a single target in such a network of cameras. The solution is robust, efficient, and scalable with the network size. It is probably the first that can cope with hundreds of cameras and thousands of objects. The spatio-temporal topology of the network is assumed to be given. In addition, an algorithm for computing features that can be used to match the appearance of the object at different time steps is assumed to be available. Due to low video quality and limitations of the computed features, possible confusion between the target and other objects can occur. The suggested method overcomes this challenge using a new modified particle filtering framework that produces at each time step a small set of candidate solutions represented by states. Each state consists of an object location and identity. Since invisible locations are explicitly modeled by states, the detection of disappearing and reappearing targets is inherent in the algorithm. A second phase recovers the path using a dynamic programing algorithm on a layered graph that consists of the computed candidate states. A synthetic system with hundreds of cameras and thousands of moving objects is generated and used to demonstrate the efficiency and robustness of the method. The results depend, as expected, on the network topologies and the confusion level between objects. For challenging cases our method obtained good results.