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Intuitively, motion blur may hurt the performance of visual object tracking.However, we lack quantitative evaluation of tracker robustness to differentlevels of motion blur. Meanwhile, while image deblurring methods can producevisually clearer videos for pleasing human eyes, it is unknown whether visualobject tracking can benefit from image deblurring or not. In this paper, weaddress these two problems by constructing a Blurred Video Tracking benchmark,which contains a variety of videos with different levels of motion blurs, aswell as ground truth tracking results for evaluating trackers. We extensivelyevaluate 23 trackers on this benchmark and observe several new interestingresults. Specifically, we find that light blur may improve the performance ofmany trackers, but heavy blur always hurts the tracking performance. We alsofind that image deblurring may help to improve tracking performance on heavilyblurred videos but hurt the performance on lightly blurred videos. According tothese observations, we propose a new GAN based scheme to improve the trackerrobustness to motion blurs. In this scheme, a finetuned discriminator is usedas an adaptive assessor to selectively deblur frames during the trackingprocess. We use this scheme to successfully improve the accuracy and robustnessof 6 trackers.

Effects of Blur and Deblurring to Visual Object Tracking