iccv iccv2013 iccv2013-420 iccv2013-420-reference knowledge-graph by maker-knowledge-mining
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Author: Jason Chang, John W. Fisher_III
Abstract: We present an integrated probabilistic model for layered object tracking that combines dynamics on implicit shape representations, topologicalshape constraints, adaptive appearance models, and layered flow. The generative model combines the evolution of appearances and layer shapes with a Gaussian process flow and explicit layer ordering. Efficient MCMC sampling algorithms are developed to enable a particle filtering approach while reasoning about the distribution of object boundaries in video. We demonstrate the utility oftheproposed tracking algorithm on a wide variety of video sources while achieving state-of-the-art results on a boundary-accurate tracking dataset.