Visual tracking plays an important role in many comput er vision tasks. A common assumption in previous methods is that the video frames are blur free. In reality, motion blurs are pervasive in the real videos. In this paper we present a novel BLUr-driven Tracker (BLUT) framework for tracking motion-blurred targets. BLUT actively uses the informa tion from blurs without performing deblurring. Specifically, we integrate the tracking problem with the motion-from-blur problem under a unified sparse approximation framework. We further use the motion information inferred by blurs to guide the sampling process in the particle filter based track ing. To evaluate our method, we have collected a large num ber of video sequences with significant motion blurs and compared BLUT with state-of-the-art trackers. Experimen tal results show that, while many previous methods are sen sitive to motion blurs, BLUT can robustly and reliably track severely blurred targets.
In REF , a novel blurdriven tracker framework for tracking motion-blurred targets is proposed.
Blurred target tracking by Blur-driven Tracker
2011 International Conference on Computer Vision
2011 International Conference on Computer Vision
Computer Science