Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Active tracking of foveated feature clusters using affine structure
International Journal of Computer Vision
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Particle Filter to Track Multiple Objects
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Tracking Articulated Body by Dynamic Markov Network
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Object Tracking with Kernel Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Decentralized Multiple Target Tracking Using Netted Collaborative Autonomous Trackers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using Particles to Track Varying Numbers of Interacting People
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting group activities using rigidity of formation
Proceedings of the 13th annual ACM international conference on Multimedia
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Applied Signal Processing
Sequential particle generation for visual tracking
IEEE Transactions on Circuits and Systems for Video Technology
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
Real-Time Decentralized Articulated Motion Analysis and Object Tracking From Videos
IEEE Transactions on Image Processing
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
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We consider a special problem of multi-target tracking, where a group of targets are highly correlated, usually demonstrating a common motion pattern with individual variations. We focus on the task of searching and provide a statistical framework of embedding the correlation among targets and the most recent observations into sampling, where the correlation is learned dynamically from the previous tracking results. Proposal distribution is updated during the sampling process fused with the motion prior and observation information. In this way, the observation of a single target is multiplexed statistically through mutual correlation among the multiple targets, and the correlation serves as both a prior information to improve the efficiency and a constraint to prevent trackers from drifting. Extensive experiments on tracking both naturally correlated and environment-constrained targets demonstrate superior and promising robust results with low complexity.