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Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameterdoi:10.1109/cvpr.2018.00061 dblp:conf/cvpr/DongSWL0P18 fatcat:vfngzqqk7bedhnvf4nbpce6tji