Robust patch-based tracking via superpixel learning

摘要

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.