Robust patch-based tracking via superpixel learning

Qianwen Li; Yue Zhou

doi:10.1117/12.2064635

16 April 2014 Robust patch-based tracking via superpixel learning

Qianwen Li, Yue Zhou

Author Affiliations +

Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 915924 (2014) https://doi.org/10.1117/12.2064635
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

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.

Citation Download Citation

Qianwen Li and Yue Zhou "Robust patch-based tracking via superpixel learning", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 915924 (16 April 2014); https://doi.org/10.1117/12.2064635

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