A survey of datasets for visual tracking <s> The BLUT dataset <s> We consider the problem of visual tracking of regions of interest in a sequence of motion blurred images. Traditional methods couple tracking with deblurring in order to correctly account for the effects of motion blur. Such coupling is usually appropriate, but computationally wasteful when visual tracking is the lone objective. Instead of deblurring images, we propose to match regions by blurring them. The matching score for two image regions is governed by a cost function that only involves the region deformation parameters and two motion blur vectors. We present an efficient algorithm to minimize the proposed cost function and demonstrate it on sequences of real blurred images. <s> BIB001 </s> A survey of datasets for visual tracking <s> The BLUT dataset <s> Visual tracking plays an important role in many computer 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 information from blurs without performing debluring. 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 tracking. To evaluate our method, we have collected a large number of video sequences with significatcant motion blurs and compared BLUT with state-of-the-art trackers. Experimental results show that, while many previous methods are sensitive to motion blurs, BLUT can robustly and reliably track severely blurred targets. <s> BIB002
The BLUT dataset [11] was proposed by Haibin Ling in 2011 to test a visual tracking algorithm dedicated to blurred properties of the video sequences, called the L 1 tracker BIB002 . This dataset is composed of seven video sequences containing various objects (cars, faces, human body or poster). They were recorded using a fast moving camera, which causes blur in the frames of the sequences. If the movement of the objects to track is quite simple, the blur considerably decreases the quality of the sequences, which makes the objects harder to track. To our knowledge, only a few works have been tested on this dataset BIB002  , even though some previous works have already addressed the blur problem BIB001 (but, unfortunately, without providing any dataset) (Fig. 3) .  The BLUT dataset [11] . Four of the seven video sequences with strong blur properties