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 BoBoT dataset 
1. Abstract of Cited Paper (BIB001):  Visual tracking is still an open problem because one needs to discriminate between the target object and background ::: under long duration. There is a major problem with conventional adaptive tracking where the target object is incorrectly ::: learnt (adapted) during runtime, resulting in poor performance of tracker. In this paper, we address this problem by ::: proposing validation-update strategy to minimize the error of false patches updating. The classifier we use is based on ::: boosted ensemble of Local Dominant Orientation (LDO). However, since LDO features contain binary values which are ::: unsuitable for classification, we have added a process to the online boosting learning algorithm that permits the two ::: binary values of "0" and "1". We elevate the tracker performance by pairing the classifier with normalized crosscorrelation ::: of patches tracked by Lukas-Kanade tracker. In the experiment conducted, we compare our method with two ::: other state-of-the-art adaptive trackers using BoBot dataset. Our method yields good tracking performance under variety ::: of scenarios set by BoBot dataset.

Literature Review:
The Bonn Benchmark on Tracking (BoBoT)  frames have a size of 320×240 pixels and their numbers vary from 305 to 1308. Ground truth annotations are also given for each sequence. They correspond to the coordinates of the target object's bounding box and its size (in text format files). A java tool evaluator enables to compare different algorithms in terms of tracking errors, with respect to a specific scheme. As shown in Fig. 4 , each sequence (named from A to L) is dedicated to highlight specific performances of the algorithms (for example, tracking specific motions such as translations, rotations, oscillations, or tracking under occlusion, appearance and disappearance of the object, illumination changes, etc.). This dataset was used in several recent works, such as BIB001    BIB002 .