Granularity and elasticity adaptation in visual tracking

13 years 1 months ago
Granularity and elasticity adaptation in visual tracking
The observation models in tracking algorithms are critical to both tracking performance and applicable scenarios but are often simplified to focus on fixed level of certain target properties such as appearances and structures. In this paper, we propose a unified tracking paradigm in which targets are represented by Markov random fields of interest regions and introduce a new way to adapt observation models by automatically tuning the feature granularity and asticity, i.e. the abstraction level of features and the model's degree of flexibility to tolerate deformations. Specifically, we employ a multi-scale scheme to extract features from interest regions and adjust the parameters of the potential functions of the MRF model to maximize the likelihoods of tracking results. Experiments demonstrate the method can estimate translation, scaling and rotation and deal with deformation, partial occlusions, and camouflage objects within this unified framework.
Ming Yang, Ying Wu
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Ming Yang, Ying Wu
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