Tracking as Repeated Figure/Ground Segmentation

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Tracking as Repeated Figure/Ground Segmentation
Tracking over a long period of time is challenging as the appearance, shape and scale of the object in question may vary. We propose a paradigm of tracking by repeatedly segmenting figure from background. Accurate spatial support obtained in segmentation provides rich information about the track and enables reliable tracking of non-rigid objects without drifting. Figure/ground segmentation operates sequentially in each frame by utilizing both static image cues and temporal coherence cues, which include an appearance model of brightness (or color) and a spatial model propagating figure/ground masks through low-level region correspondence. A superpixel-based conditional random field linearly combines cues and loopy belief propagation is used to estimate marginal posteriors of figure vs background. We demonstrate our approach on long sequences of sports video, including figure skating and football.
Xiaofeng Ren, Jitendra Malik
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Xiaofeng Ren, Jitendra Malik
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