Active shape models—their training and application
Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Probabilistic Tracking with Exemplars in a Metric Space
International Journal of Computer Vision - Marr Prize Special Issue
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Graphical Models of Images, Videos and Their Spatial Transformations
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Synthesis by Non-Parametric Sampling
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
3D Tracking = Classification + Interpolation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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We propose an exemplar-based tracking framework for human contour tracking. The exemplars, i.e. the contour representatives, are used to construct a contour ensemble. The main task of contour ensemble is to generate contours to fill in the gaps in-between in the test sequences, and to supply the dynamics for updating the target contour by fast contour query. As a result, a normal dynamic Bayesian network is only used to infer the location and the size of the target contour. The effectiveness of the proposed method is tested by many human motion sequences.