Tracking as Repeated Figure/Ground Segmentation
Xiaofeng Ren
Malik, J.
Toyota Technol. Inst. at Chicago, Chicago;
This paper appears in: Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
Publication Date: 17-22 June 2007
On page(s): 1-8
Location: Minneapolis, MN,
ISBN: 1-4244-1180-7
INSPEC Accession Number: 9738066
Digital Object Identifier: 10.1109/CVPR.2007.383177
Current Version Published: 2007-07-16
Abstract
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.
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