Tracking and data association
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
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Tracking with Exemplars in a Metric Space
International Journal of Computer Vision - Marr Prize Special Issue
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
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
A Graphical Model for Audiovisual Object Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Particle Filter to Track Multiple Objects
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
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
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
A vision-based system for display interaction
Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Shape based appearance model for kernel tracking
Image and Vision Computing
Visual tracking via adaptive tracker selection with multiple features
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Dynamic objectness for adaptive tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Disagreement-Based multi-system tracking
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Over the past few years researchers have been investigating the enhancement of visual tracking performance by devising trackers that simultaneously make use of several different features. In this paper we investigate the combination of synchronous visual trackers that use different features while treating the trackers as "black boxes". That is, instead of fusing the usage of the different types of data as has been performed in previous work, the combination here is allowed to use only the trackers' output estimates, which may be modified before their propagation to the next time step. We propose a probabilistic framework for combining multiple synchronous trackers, where each separate tracker outputs a probability density function of the tracked state, sequentially for each image. The trackers may output either an explicit probability density function, or a sample-set of it via Condensation. Unlike previous tracker combinations, the proposed framework is fairly general and allows the combination of any set of trackers of this kind, even in different state-spaces of different dimensionality, under a few reasonable assumptions. The combination may consist of different trackers that track a common object, as well as trackers that track separate, albeit related objects, thus improving the tracking performance of each object. The benefits of merely using the final estimates of the separate trackers in the combination are twofold. Firstly, the framework for the combination is fairly general and may be easily used from the software aspects. Secondly, the combination may be performed in a distributed setting, where each separate tracker runs on a different site and uses different data, while avoiding the need to share the data. The suggested framework was successfully tested using various state-spaces and datasets, demonstrating that fusing the trackers' final distribution estimates may indeed be applicable.