Fusing Multiple Independent Estimates via Spectral Clustering for Robust Visual Tracking

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One fundamental problem of object tracking is the convergence of estimates to local maxima not corresponding to target objects. To mitigate this problem, constructing a good posterior distribution of the target state is important. In this letter, we propose a robust tracking approach by building a new posterior distribution model from multiple independent estimates of a target state. For each candidate of the target state, we compute a confidence score based on its spatial consistency with other estimates and photometric similarities with target models. Our posterior distribution model reflects tracking uncertainties well and adaptively defines the search region for the next frame. We validate the robustness of our approach on a number of challenging datasets.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2012-08
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.19, no.8, pp.527 - 530

ISSN
1070-9908
DOI
10.1109/LSP.2012.2205916
URI
http://hdl.handle.net/10203/102826
Appears in Collection
EE-Journal Papers(저널논문)
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