Level set tracking with dynamical shape priors

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Level set tracking with dynamical shape priors
Dynamical shape priors are curical for level set-based nonrigid object tracking with noise, occlusions or background clutter. In this paper, we propose a level set tracking framework using dynamical shape priors to capture contours changes of an object in a periodic action sequence. The framework consists of two stages— off-line training and on-line tracking. During the off-line training stage, a graphbased dominant set clustering (DSC) method is applied to learn a shape codebook with each codeword representing a certain shape mode. Then a codeword transition matrix is learnt to characterize the temporal correlations of contours of an object. During the on-line tracking stage, we fuse the knowledge of shape priors and current observations, and adopt maximum a posteriori (MAP) estimation to predict the current shape mode. The experimental results on synthetic and real video sequences demonstrate the effectiveness of our method.
Xue Zhou, Xi Li, Weiming Hu
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICIP
Authors Xue Zhou, Xi Li, Weiming Hu
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