A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Algorithmic Fusion for More Robust Feature Tracking
International Journal of Computer Vision
Combining Multiple Motion Estimates for Vehicle Tracking
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A general filter for measurements with any probability distribution
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Self-Organized Integration of Adaptive Visual Cues for Face Tracking
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Representation of Uncertainty in Spatial Target Tracking
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A probabilistic framework for combining tracking algorithms
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A General Framework for Combining Visual Trackers --- The "Black Boxes" Approach
International Journal of Computer Vision
Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks
Journal of VLSI Signal Processing Systems
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
Machine Vision and Applications
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
A probabilistic framework for combining tracking algorithms
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
Robust tracking with and beyond visible spectrum: a four-layer data fusion framework
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
A framework to integrate particle filters for robust tracking in non-stationary environments
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
A large margin framework for single camera offline tracking with hybrid cues
Computer Vision and Image Understanding
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For the past few years researches have been investigating enhancing tracking performance by combining several different tracking algorithms. We propose an analytically justified, probabilistic framework to combine multiple tracking algorithms. The separate tracking algorithms considered output a probability distribution function of the tracked state, sequentially for each image. The algorithms may output either an explicit probability distribution function, or a sample-set of it via CONDENSATION. The proposed framework is general and allows the combination of any set of separate tracking algorithms of this kind, even on different state spaces of different dimensionality, under a few reasonable assumptions. In many of the investigated settings, our approach allows us to treat the separate tracking algorithms as "closed boxes". In other words, only the state distributions in the input and output are needed for the combination process. The suggested framework was successfully tested using various state spaces and datasets.