Siamese Learning Visual Tracking: A Survey <s> RELATED WORK <s> The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects. <s> BIB001 </s> Siamese Learning Visual Tracking: A Survey <s> RELATED WORK <s> This report contains a review of visual tracking in monocular video sequences. For the purpose of this review, the majority of the visual trackers in the literature are divided into three tracking categories: discrete feature trackers, contour trackers, and region-based trackers. This categorization was performed based on the features used and the algorithms employed by the various visual trackers. The first class of trackers represents targets as discrete features (e.g. points, sets of points, lines) and performs data association using a distance metric that accommodates the particular feature. Contour trackers provide precise outlines of the target boundaries, meaning that they must not only uncover the position of the target, but its shape as well. Contour trackers often make use of gradient edge information during the tracking process. Region trackers represent the target with area-based descriptors that define its support and attempt to locate the image region in the current frame that best matches an object template. Trackers that are not in agreement with the abovementioned categorization, including those that combine methods from the three defined classes, are also considered in this review. In addition to categorizing and describing the various visual trackers in the literature, this review also provides a commentary on the current state of the field as well as a comparative analysis of the various approaches. The paper concludes with an outline of open problems in visual tracking. <s> BIB002 </s> Siamese Learning Visual Tracking: A Survey <s> RELATED WORK <s> The goal of this paper is to review the state-of-the-art progress on visual tracking methods, classify them into different categories, as well as identify future trends. Visual tracking is a fundamental task in many computer vision applications and has been well studied in the last decades. Although numerous approaches have been proposed, robust visual tracking remains a huge challenge. Difficulties in visual tracking can arise due to abrupt object motion, appearance pattern change, non-rigid object structures, occlusion and camera motion. In this paper, we first analyze the state-of-the-art feature descriptors which are used to represent the appearance of tracked objects. Then, we categorize the tracking progresses into three groups, provide detailed descriptions of representative methods in each group, and examine their positive and negative aspects. At last, we outline the future trends for visual tracking research. <s> BIB003 </s> Siamese Learning Visual Tracking: A Survey <s> RELATED WORK <s> Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source code and video datasets) are examined in this survey. <s> BIB004 </s> Siamese Learning Visual Tracking: A Survey <s> RELATED WORK <s> There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers. <s> BIB005
Five significant surveys of tracking have been published recently BIB001 , BIB002 , BIB003 , BIB004 , BIB005 . Yilmaz et al. BIB001 proposed a first taxonomy of tracking and proposed important challenges for trackers such as unconstrained video, context, efficient inference and application specific fine-tuning online either discriminative or un-/semisupervised by using machine learning. Cannons BIB002 gives a comprehensive overview of building blocks such as representation, initialisation, prediction, association and adaptation. The paper discusses pros and cons of features such as points, edges, contours, regions and their combinations. The work identifies as important challenges for future research feature selection i.e. the representation, the evolution of the representation over longer time periods, the combination of trackers and tracker evaluation. Yang et al. BIB003 review global and local features, the integration of object context and online generative versus discriminative learning. The work identifies ensemble learning to combine heterogeneous features into a coherent inference framework where particle filters are compared in detail. The work states the drift problem as important problem and proposes as future research consideration of adaptive priors, object context, pixel-wise segmentation, both generative and discriminative learning and SMC methods for inference. Li et al. BIB004 survey solely representation with a summary of global and local features and a review on generative, discriminative and hybrid learning approaches. The survey concludes with the inevitable trade-off between robustness and accuracy by carefully balancing representation and prior information. The work proposes the need for a principled approach to reconstruct 3-D pose, the need for viewpoint and frame-rate invariance and the need for attentive mechanisms for initialisation similar to human vision. Finally, Smeulders et al. BIB005 compare 19 online trackers by their predicted bounding boxes on the ALOV benchmark. The paper concludes with the importance of the experimental setup which is crucial to assess performance and that tracking unknown objects in unknown scenarios is an open problem. The experiments show little conceptual consensus among the trackers and find none of the trackers superior. There is evidence that complex representations are inferior to simpler ones. Sparse and local features seem appropriate to handle rapid changes such as occlusion, clutter and illumination.