Tracking and data association
Active vision
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning Dynamics of Complex Motions from Image Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-time tracking of image regions with changes in geometry and illumination
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
International Journal of Computer Vision
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
A non-invasive computer vision system for reliable eye tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Multiple Hypothesis Tracking for Automatic Optical Motion Capture
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Towards Real-Time Cue Integration by Using Partial Results
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Hierarchical Vision Architecture for Robotic Manipulation Tasks
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Tracking of Moving Heads in Cluttered Scenes from Stereo Vision
RobVis '01 Proceedings of the International Workshop on Robot Vision
Robust tracking of human body parts for collaborative human computer interaction
Computer Vision and Image Understanding
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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
MAP ZDF segmentation and tracking using active stereo vision: Hand tracking case study
Computer Vision and Image Understanding
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
A novel face and hands tracking in a complex background
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Machine Vision and Applications
Automated tracking in digitized videofluoroscopy sequences for spine kinematic analysis
Image and Vision Computing
Adaptive agent based system for state estimation using dynamic multidimensional information sources
IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Spatio-temporal attention mechanism for more complex analysis to track multiple objects
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Robust decentralized multi-model adaptive template tracking
Pattern Recognition
Pattern Recognition Letters
Touch tracking with a particle filter
Machine Vision and Applications
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Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for combining and sharing information among several state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to disparate visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments focusing on color-region- and snake-based tracking that demonstrate the efficacy of this approach.