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 Ross dataset 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation over a template set, which leads to the so-called L1 trackers as it needs to solve an l 1 norm related minimization problem for many times. While these L1 trackers showed impressive tracking accuracies, they are very computationally demanding and the speed bottleneck is the solver to l 1 norm minimizations. This paper aims at developing an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers. In our proposed L1 tracker, a new l 1 norm related minimization model is proposed to improve the tracking accuracy by adding an l 1 norm regularization on the coefficients associated with the trivial templates. Moreover, based on the accelerated proximal gradient approach, a very fast numerical solver is developed to solve the resulting l 1 norm related minimization problem with guaranteed quadratic convergence. The great running time efficiency and tracking accuracy of the proposed tracker is validated with a comprehensive evaluation involving eight challenging sequences and five alternative state-of-the-art trackers. 
3. Abstract of Cited Paper (BIB003):  Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. 
4. Abstract of Cited Paper (BIB004):  A speeded up robust features (SURF) based optical flow algorithm is presented for visual tracking in real scenarios. SURF construct invariant features to correspond the blobs of interest across frames. Meanwhile, new feature-based optical flow algorithm is used to compute the warp matrix of a region centered on SURF key points. Furthermore, on-line visual learning for long-term tracking is performed using incremental object subspace method, which includes the correct update of the sample mean and appearance model. The proposed SURF based tracking and learning method contributes measurably to improving overall tracking performance. Experimental work demonstrates that the proposed strategy improves the performance of the classical optical flow algorithms in complicated real scenarios. 
5. Abstract of Cited Paper (BIB005):  This paper presents a method of extracting discriminative stable regions (DSRs) from image, and applies them for object tracking. These DSRs obtained by using the criterion of maximal entropy and spatial discrimination present high appearance stability and strong spatial discriminative power, which enables them to tolerate more appearance variations and to effectively resist spatial distracters. Meanwhile, the adaptive fusion tracking incorporated k-means clustering can handle severe occlusion as well as disturbance of motion noise during target localization. In addition, an effective local update scheme is designed to adapt to the object change for ensuring the tracking robustness. Experiments are carried out on several challenging sequences and results show that our method performs well in terms of object tracking, even in the presence of occlusion, deformation, illumination change, moving camera and spatial distracter. 
6. Abstract of Cited Paper (BIB006):  Recently, sparse representation in the task of visual tracking has been obtained increasing attention and many algorithms are proposed based on it. In these algorithms for visual tracking, each candidate target is sparsely represented by a set of target templates. However, these algorithms fail to consider the structural information of the space of the target templates, i.e., target template set. In this paper, we propose an algorithm named non-local self-similarity (NLSS) based sparse coding algorithm (NLSSC) to learn the sparse representations, which considers the geometrical structure of the set of target candidates. By using non-local self-similarity (NLSS) as a smooth operator, the proposed method can turn the tracking into sparse representations problems, in which the information of the set of target candidates is exploited. Extensive experimental results on visual tracking have demonstrated the effectiveness of the proposed algorithm. 
7. Abstract of Cited Paper (BIB007):  Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the target. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the target variations over time. We then rigorously show that this model is a special case of the Earth Mover's Distance optimization problem where the ground distance is governed by some underlying noise model. This noise model has several parameters that control the cost of moving pixels and changing their color. We develop two such noise models and demonstrate how their parameters can be estimated on-line during tracking to account for the amount of local (dis)order in the target. We also discuss the significance of this on-line parameter update and demonstrate its contribution to the performance. Finally we show LOT's tracking capabilities on challenging video sequences, both commonly used and new, displaying performance comparable to state-of-the-art methods. 
8. Abstract of Cited Paper (BIB008):  Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.

Literature Review:
The Ross dataset [40] was proposed in 2007 to test the robustness of visual tracking algorithms in situations of strong appearance changes. This dataset contains seven sequences, without any ground truth. They are comparable to those of FRAGtrack and MILtrack, but they contain some specificities, such as highly cluttered background (see Sequence Car 11, Fig. 19, for example) , or illumination changes (Sequence Fish, Fig. 19 , is only dedicated to test illumination changes: neither the object nor the camera are moving). Some other sites also propose complete ground truths for some of these sequences: David Indoor (one of the most famous sequence)  , Car 5, Car 11, Dudek, Sylvester, Dog and Treillis  . This dataset is also frequently used for testing visual tracking algorithms  BIB009 BIB002 BIB008  BIB001 BIB003 BIB006  BIB004 BIB007 BIB005 . 20 The SPOT dataset [45] . Four of the six video sequences with multiple and often similar objects to track lected from Youtube. The originality of this dataset lies in the fact that it is dedicated to track simultaneously multiple objects, sometimes with similar appearances (see Fig. 20 ).