A survey of datasets for visual tracking <s> The Ellis dataset <s> A novel approach to learning and tracking arbitrary image features is presented. Tracking is tackled by learning the mapping from image intensity differences to displacements. Linear regression is used, resulting in low computational cost. An appearance model of the target is built on-the-fly by clustering sub-sampled image templates. The medoidshift algorithm is used to cluster the templates thus identifying various modes or aspects of the target appearance, each mode is associated to the most suitable set of linear predictors allowing piecewise linear regression from image intensity differences to warp updates. Despite no hard-coding or offline learning, excellent results are shown on three publicly available video sequences and comparisons with related approaches made. <s> BIB001 </s> A survey of datasets for visual tracking <s> The Ellis dataset <s> 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. <s> BIB002
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 .