Effects of Blur and Deblurring to Visual Object Tracking
This work addresses the problem of tracker robustness to blur for computer vision researchers, providing a benchmark and adaptive method, but it is incremental as it builds on existing tracking and deblurring techniques.
The paper quantifies how motion blur affects visual object tracking by creating a benchmark with varying blur levels, finding that light blur can improve tracker performance while heavy blur harms it, and that deblurring helps only for heavy blur. It proposes a GAN-based scheme using a discriminator to selectively deblur frames, improving accuracy and robustness for 6 trackers.
Intuitively, motion blur may hurt the performance of visual object tracking. However, we lack quantitative evaluation of tracker robustness to different levels of motion blur. Meanwhile, while image deblurring methods can produce visually clearer videos for pleasing human eyes, it is unknown whether visual object tracking can benefit from image deblurring or not. In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers. We extensively evaluate 23 trackers on this benchmark and observe several new interesting results. Specifically, we find that light blur may improve the performance of many trackers, but heavy blur always hurts the tracking performance. We also find that image deblurring may help to improve tracking performance on heavily blurred videos but hurt the performance on lightly blurred videos. According to these observations, we propose a new GAN based scheme to improve the tracker robustness to motion blurs. In this scheme, a finetuned discriminator is used as an adaptive assessor to selectively deblur frames during the tracking process. We use this scheme to successfully improve the accuracy and robustness of 6 trackers.