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Proceedings Paper

Robust patch-based tracking via superpixel learning
Author(s): Qianwen Li; Yue Zhou
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Paper Abstract

Aimed at tracking non-rigid objects with geometric appearance changes over time, we propose a novel patch-based appearance model to adapt to the changes of topology. Meanwhile, as an effective online updating scheme, superpixel learning is adopted to select and update the patches when a new frame arrives. We build a foreground-background vote map via superpixels to determine the confidence of the patches in case of drifting. Experimental results show the proposed approach enables tracking non-rigid targets robustly and accurately.

Paper Details

Date Published: 16 April 2014
PDF: 5 pages
Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 915924 (16 April 2014); doi: 10.1117/12.2064635
Show Author Affiliations
Qianwen Li, Shanghai Jiao Tong Univ. (China)
Yue Zhou, Shanghai Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 9159:
Sixth International Conference on Digital Image Processing (ICDIP 2014)
Charles M. Falco; Chin-Chen Chang; Xudong Jiang, Editor(s)

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