You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey of datasets for visual tracking 
Chapter Title:  The Kwon datasets 
1. Abstract of Cited Paper (BIB001):  We propose a novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently. The experiment demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments and outperforms the state-of-the-art tracking methods. 
2. Abstract of Cited Paper (BIB002):  In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms. 
3. Abstract of Cited Paper (BIB003):  In this paper, we propose a biologically inspired framework of visual tracking based on proto-objects. Given an image sequence, proto-objects are first detected by combining saliency map and topic model. Then the target is tracked based on spatial and saliency information of the proto-objects. In the proposed Bayesian approach, states of the target and proto-objects are jointly estimated over time. Gibbs sampling has been used to optimize the estimation during the tracking process. The proposed method robustly handles occlusion, distraction, and illumination change in the experiments. Experimental results also demonstrate that the proposed method outperforms the state-of-the-art methods in challenging tracking tasks.

Literature Review:
Junseok Kwon made publicly available three datasets. . It contains four sequences in which the geometric appearance of the object to track drastically changes as well as the background while the camera is moving. The ground truth annotations are given as the center and size of the box surrounding the object. The third dataset, proposed in 2010, can be found on two websites   . It contains a total of six sequences with severe occlusions, pose variations, illumination changes, and abrupt motions. The ground truth is also given (manual detection of the ellipse surrounding the object to track). Frames from these different datasets are given in Fig. 12 . All these video sequences are probably, today, the hardest to work with. For example, sequence Soccer shows victorious soccer players filmed by a moving camera. They are sometimes occluded by a red smoke, making them really hard to track. However, a lot of recent works have validated their algorithms on these datasets BIB001 BIB003 BIB002 BIB004