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Minimum Uncertainty Gap for Robust Visual Tracking
2013 IEEE Conference on Computer Vision and Pattern Recognition
We propose a novel tracking algorithm that robustly tracks the target by finding the state which minimizes uncertainty of the likelihood at current state. The uncertainty of the likelihood is estimated by obtaining the gap between the lower and upper bounds of the likelihood. By minimizing the gap between the two bounds, our method finds the confident and reliable state of the target. In the paper, the state that gives the Minimum Uncertainty Gap (MUG) between likelihood bounds is shown to bedoi:10.1109/cvpr.2013.305 dblp:conf/cvpr/KwonL13 fatcat:7an3a6hnnfa5nb54i3k3uhqaky