CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking in Clutter: A Subset Approach
International Journal of Computer Vision
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Accurate, Real-Time, Unadorned Lip Tracking
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Unsupervised lip segmentation under natural conditions
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Learning-based tracking of complex non-rigid motion
Journal of Computer Science and Technology
Lips tracking biometrics for speaker recognition
International Journal of Biometrics
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In this paper a new Condensation style contour tracking method called Probabilistic Dynamic Contour (PDC) is proposed for lip tracking: a novel mixture dynamic model is designed to represent shape more compactly and to tolerate larger motions between frames, a measurement model is designed to include multiple visual cues. The proposed PDC tracker has the advantage that it is conceptually general but effectively suitable for lip tracking with the designed dynamic and measurement model. The new tracker improves the traditional Condensation style tracker in three aspects: Firstly, the dynamic model is partially derived from the image sequence, so the tracker does not need to learn the dynamics in advance. Secondly, the measurement model is easy to be updated during tracking, which avoids modeling the foreground object in prior. Thirdly, to improve the tracker's speed, a compact representation of shape and a noise model are proposed to reduce the samples required to represent the posterior distribution. Experiment on lip contour tracking shows that the proposed method tracks contour robustly as well as accurately compare to the existing tracking method.