Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Tracking with an Adaptive Color-Based Particle Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Real-Time Tracking with Multiple Cues by Set Theoretic Random Search
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selecting Discriminative Tracking Features Using Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online learning of region confidences for object tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Video object segmentation and tracking using ψ-learning classification
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
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
Efficient visual tracking using particle filter with incremental likelihood calculation
Information Sciences: an International Journal
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A novel probabilistic tracking system is presented, which includes a sequential particle sampler and a fragment-based measurement model. Rather than generating particles independently in a generic particle filter, the correlation between particles is used to improve sampling efficieney, especially when the target moves in an unexpected and abrupt fashion. We propose to update the proposal distribution by dynamically incorporating the most recent measurements and generating particles sequentially, where the contextual confidence of the user on the measurement model is also involved. Besides, the matching template is divided into non-overlapping fragments, and by learning the background information only a subset of the most discriminative target regions are dynamically selected to measure each particle, where the model update is easily embedded to handle fast appearance cbanges. The two parts are dynamically fused together such that the system is able to capture abrupt motions and produce a better localization of the moving target in an efficient way. With the improved discriminative power, the new algorithm also succeeds in handling partial occlusions and clutter background. Experiments on both synthetic and real-world data verify the effectiveness of the new algorithm and demonstrate its superiority over existing methods.