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 Ellis dataset 
1. Abstract of Cited Paper (BIB001):  This work proposes an approach to tracking by regression that uses no hard-coded models and no offline learning stage. The Linear Predictor (LP) tracker has been shown to be highly computationally efficient, resulting in fast tracking. Regression tracking techniques tend to require offline learning to learn suitable regression functions. This work removes the need for offline learning and therefore increases the applicability of the technique. The online-LP tracker can simply be seeded with an initial target location, akin to the ubiquitous Lucas-Kanade algorithm that tracks by registering an image template via minimisation. ::: ::: A fundamental issue for all trackers is the representation of the target appearance and how this representation is able to adapt to changes in target appearance over time. The two proposed methods, LP-SMAT and LP-MED, demonstrate the ability to adapt to large appearance variations by incrementally building an appearance model that identifies modes or aspects of the target appearance and associates these aspects to the Linear Predictor trackers to which they are best suited. Experiments comparing and evaluating regression and registration techniques are presented along with performance evaluations favourably comparing the proposed tracker and appearance model learning methods to other state of the art simultaneous modelling and tracking approaches.

Literature Review:
Liam Ellis proposed this dataset  in 2008. It contains three grayscale video sequences in which the object to track changes its appearance with time, due to strong motions of the object and/or the camera. Sequences Head Motion and Camera Shake (see Fig. 8 ) are not of a high complexity for visual tracking (except for the blur that can appear in case of fast movements). On the opposite, Sequence Track Running is very challenging because the point of view of the camera evolves with time, sometimes showing the runner full face, sometimes 3/4 face, and sometimes it even zooms on him. Moreover, the background is cluttered and some people are also running near the tracked runner. All sequences are provided with ground truth corresponding to the center and size of the bounding boxes in each frame. To our knowledge, this dataset was only used by its providers BIB002 BIB001 .