EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th 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
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
Convex Optimization
Bi-Directional Tracking Using Trajectory Segment Analysis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Interactive Feature Tracking using K-D Trees and Dynamic Programming
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Tracking by Affine Kernel Transformations Using Color and Boundary Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean shift blob tracking with kernel histogram filtering and hypothesis testing
Pattern Recognition Letters
Online updating appearance generative mixture model for meanshift tracking
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Getting robust observation for single object tracking: a statistical Kernel-based approach
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Fragments based tracking with adaptive cue integration
Computer Vision and Image Understanding
Robust hierarchical multiple hypothesis tracker for multiple-object tracking
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
Markov Chain Monte Carlo Modular Ensemble Tracking
Image and Vision Computing
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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The Mean Shift tracker is a widely used tool for robustly and quickly tracking the location of an object in an image sequence using the object's color histogram. The reference histogram is typically set to that in the target region in the frame where the tracking is initiated. Often, however, no single view suffices to produce a reference histogram appropriate for tracking the target. In contexts where multiple views of the target are available prior to the tracking, this paper enhances the Mean Shift tracker to use multiple reference histograms obtained from these different target views. This is done while preserving both the convergence and the speed properties of the original tracker. We first suggest a simple method to use multiple reference histograms for producing a single histogram that is more appropriate for tracking the target. Then, to enhance the tracking further, we propose an extension to the Mean Shift tracker where the convex hull of these histograms is used as the target model. Many experimental results demonstrate the successful tracking of targets whose visible colors change drastically and rapidly during the sequence, where the basic Mean Shift tracker obviously fails.