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. An experiment on lip contour tracking shows that the proposed method tracks contours robustly as well as accurately compared to the existing tracking method.
['Qiang Wang', 'Haizhou Ai', 'Guangyou Xu']
A probabilistic dynamic contour model for accurate and robust lip tracking