EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
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
Hyperdynamics Importance Sampling
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Performance Evaluation of People Tracking Systems
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Performance Evaluation of Object Detection Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Tools and Techniques for Video Performance Evaluation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Real-Time Tracking Using Trust-Region Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation Metrics for Motion Detection and Tracking
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Kernel-Based Bayesian Filtering for Object Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Performance evaluation metrics and statistics for positional tracker evaluation
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Automated vision tracking of project related entities
Advanced Engineering Informatics
Pose robust face tracking by combining view-based AAMs and temporal filters
Computer Vision and Image Understanding
Computers & Mathematics with Applications
Multi-part sparse representation in random crowded scenes tracking
Pattern Recognition Letters
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We propose a tracking algorithm that combines the Mean Shift search in a Particle Filtering framework and a target representation that uses multiple semi-overlapping color histograms. The target representation introduces spatial information that accounts for rotation and anisotropic scaling without compromising the flexibility typical of color histograms. Moreover, the proposed tracker can generate a smaller number of samples than Particle Filter as it increases the particle efficiency by moving the samples toward close local maxima of the likelihood using Mean Shift. Experimental results show that the proposed representation improves the robustness to clutter and that, especially on highly maneuvering targets, the combined tracker outperforms Particle Filter and Mean Shift in terms of accuracy in estimating the target size and position while generating only 25% of the samples used by Particle Filter.