Current progress in discriminative object tracking
Abstract
Recently, great progesses have been made in using discriminative classifiers in object tracking. More specifically, correlation filters (CFs) for visual tracking have been attractive due to t heir competitive performances on both accuracy and robustness. In this paper, the latest and representative approaches of CF based trackers are presented in detail. In addition, trackers used deep convolutional features are introduced and several famous tracking methods which fine-tune the pretrained deep network are presented. To evaluate the performances of different trackers, a detailed introduction of the evaluation methodology and the datasets is described, and all introduced trackers are compared based on the mentioned datasets. Finally, several promising directions as the conclusions are drawn in this paper.
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