CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Contours: The Application of Techniques from Graphics,Vision,Control Theory and Statistics to Visual Tracking of Shapes in Motion
Tracking Many Objects with Many Sensors
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Object Tracking with an Adaptive Color-Based Particle Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Architectures for efficient implementation of particle filters
Architectures for efficient implementation of particle filters
Multi-camera calibration, object tracking and query generation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Tracking human movement patterns using particle filtering
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras
Computer Vision and Image Understanding
Camera handoff with adaptive resource management for multi-camera multi-object tracking
Image and Vision Computing
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera network. Several known techniques rely on Bayesian approaches to perform the matching task. However, these approaches do not scale well when the dimension of the problem increases; e.g. when the number of subject or possible path increases. The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking). The particle filter tracking system utilizes a map (known environment) to assist the tracking process when targets leave the field of view of any camera. We implemented and tested this tracking approach in an in-house multiple cameras system as well as using on-line data. Promising results were obtained which suggested the feasibility of such an approach.