Tracking and data association
Elements of information theory
Elements of information theory
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
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)
Multi-Camera Multi-Person Tracking for EasyLiving
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Region tracking through image sequences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
An Information Fusion Framework for Robust Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaboration of spatial and feature attention for visual tracking
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A comparative evaluation of template and histogram based 2d tracking algorithms
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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We present a Bayesian approach to real-time object tracking using nonparametric density estimation. The target model and candidates are represented by probability densities in the joint spatial-intensity domain. The new location and appearance of the target are jointly derived by computing the maximum likelihood estimate of the parameter vector that characterizes the transformation from the candidate to the model. This probabilistic formulation accommodates variations in the target appearance, while being robust to outliers represented by partial occlusions. In this paper we analyze the simplest parameterization represented by translation in both domains and present a gradient-based iterative solution. Various tracking sequences demonstrate the superior behavior of the method.