Robust tracking of human body parts for collaborative human computer interaction
Computer Vision and Image Understanding
An introduction to variable and feature selection
The Journal of Machine Learning Research
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
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
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Spatio-Temporal Context for Robust Multitarget Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A Bayesian Approach to Multiple Target Detection and Tracking
IEEE Transactions on Signal Processing
Extended Object Tracking Using Monte Carlo Methods
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Computers & Mathematics with Applications
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Multiple Object Tracking (MOT) poses three challenges to conventional well-studied Single Object Tracking (SOT) algorithms: 1) Multiple targets lead the configuration space to be exponential to the number of targets; 2) Multiple motion conditions due to multiple targets' entering, exiting and intersection make the prediction process degrade in precision; 3) Visual ambiguities among nearby targets make the trackers error prone. In this paper, we address the MOT problem by embedding contextual proposal distributions and contextual observation models into a mixture tracker which is implemented in a Particle Filter framework. The proposal distributions are adaptively selected by motion conditions of targets which are determined by context information, and the multiple features are combined according to their discriminative power between ambiguity prone objects. The induction of contextual proposal distribution and observation model can help to surmount the incapability of conventional mixture tracker in handling object occlusions, meanwhile retain its merits of flexibility and high efficiency. The final experiments show significant improvement in variable number objects tracking scenarios compared with other methods.