Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Bayesian Object Localisation in Images
International Journal of Computer Vision
Probabilistic Tracking with Exemplars in a Metric Space
International Journal of Computer Vision - Marr Prize Special Issue
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Robust Correlation Measure for Correspondence Estimation
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
A new approach to the template update problem
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Object Tracking Based on an Effective 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
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
IEEE Transactions on Image Processing
Adaptive Multifeature Tracking in a Particle Filtering Framework
IEEE Transactions on Circuits and Systems for Video Technology
Dynamic Proposal Variance and Optimal Particle Allocation in Particle Filtering for Video Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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Visual tracking of arbitrary targets in clutter is important for a wide range of military and civilian applications. We propose a general framework for the tracking of scaled and partially occluded targets, which do not necessarily have prominent features. The algorithm proposed in the present paper utilizes a modified normalized cross-correlation as the likelihood for a particle filter. The algorithm divides the template, selected by the user in the first video frame, into numerous patches. The matching process of these patches by particle filtering allows one to handle the target's occlusions and scaling. Experimental results with fixed rectangular templates show that the method is reliable for videos with nonstationary, noisy, and cluttered background, and provides accurate trajectories in cases of target translation, scaling, and occlusion.