Tracking and data association
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Statistical Foreground Modelling for Object Localisation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Probabilistic Background Model for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Dynamic Bayesian Network Approach to Tracking Using Learned Switching Dynamic Models
HSCC '00 Proceedings of the Third International Workshop on Hybrid Systems: Computation and Control
Object Localization by Bayesian Correlation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Probabilistic Contour Discriminant for Object Localisation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Variational Maximum A Posteriori by Annealed Mean Field Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyzing and Capturing Articulated Hand Motion in Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Contour graph based human tracking and action sequence recognition
Pattern Recognition
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Switching Hidden Markov Models for Learning of Motion Patterns in Videos
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A video-based indoor occupant detection and localization algorithm for smart buildings
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
International Journal of Computational Vision and Robotics
Exemplar-Based human contour tracking
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
A hierarchical dynamic bayesian network approach to visual tracking
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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We propose a generative model approach to contour tracking against non-stationary clutter and to coping with occlusions by explicit modelling and inferring. The proposed dynamic Bayesian networks consist of multiple hidden processes which model the target, the clutter and the occlusions. The image observation models, which depict the generation of the image features, are conditioned on all the hidden processes. Based on this framework, the tracker can automatically switch among different observation models according to the hidden states of the clutter and occlusions. In addition, the inference of these hidden states provides self-evaluations for the tracker. The tracking and inferencing are implemented based on sequence Monte Carlo techniques. The effectiveness of the proposed approach to robust tracking and inferring non-stationary clutter and occlusion is demonstrated for a variety of image sequences.