Visual Tracking Algorithms Using Different Object Representation Schemes

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Visual Tracking Algorithms Using Different Object Representation Schemes

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Author : Shreyamsha Kumar Bidare Kantharajappa
language : en
Publisher:
Release Date : 2019

Visual Tracking Algorithms Using Different Object Representation Schemes written by Shreyamsha Kumar Bidare Kantharajappa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Visual tracking, being one of the fundamental, most important and challenging areas in computer vision, has attracted much attention in the research community during the past decade due to its broad range of real-life applications. Even after three decades of research, it still remains a challenging problem in view of the complexities involved in the target searching due to intrinsic and extrinsic appearance variations of the object. The existing trackers fail to track the object when there are considerable amount of object appearance variations and when the object undergoes severe occlusion, scale change, out-of-plane rotation, motion blur, fast motion, in-plane rotation, out-of-view and illumination variation either individually or simultaneously. In order to have a reliable and improved tracking performance, the appearance variations should be handled carefully such that the appearance model should adapt to the intrinsic appearance variations and be robust enough for extrinsic appearance variations. The objective of this thesis is to develop visual object tracking algorithms by addressing the deficiencies of the existing algorithms to enhance the tracking performance by investigating the use of different object representation schemes to model the object appearance and then devising mechanisms to update the observation models. A tracking algorithm based on the global appearance model using robust coding and its collaboration with a local model is proposed. The global PCA subspace is used to model the global appearance of the object, and the optimum PCA basis coefficients and the global weight matrix are estimated by developing an iteratively reweighted robust coding (IRRC) technique. This global model is collaborated with the local model to exploit their individual merits. Global and local robust coding distances are introduced to find the candidate sample having similar appearance as that of the reconstructed sample from the subspace, and these distances are used to define the observation likelihood. A robust occlusion map generation scheme and a mechanism to update both the global and local observation models are developed. Quantitative and qualitative performance evaluations on OTB-50 and VOT2016, two popular benchmark datasets, demonstrate that the proposed algorithm with histogram of oriented gradient (HOG) features generally performs better than the state-of-the-art methods considered do. In spite of its good performance, there is a need to improve the tracking performance in some of the challenging attributes of OTB-50 and VOT2016. A second tracking algorithm is developed to provide an improved performance in situations for the above mentioned challenging attributes. The algorithms is designed based on a structural local 2DDCT sparse appearance model and an occlusion handling mechanism. In a structural local 2DDCT sparse appearance model, the energy compaction property of the transform is exploited to reduce the size of the dictionary as well as that of the candidate samples in the object representation so that the computational cost of the l_1-minimization used could be reduced. This strategy is in contrast to the existing models that use raw pixels. A holistic image reconstruction procedure is presented from the overlapped local patches that are obtained from the dictionary and the sparse codes, and then the reconstructed holistic image is used for robust occlusion detection and occlusion map generation. The occlusion map thus obtained is used for developing a novel observation model update mechanism to avoid the model degradation. A patch occlusion ratio is employed in the calculation of the confidence score to improve the tracking performance. Quantitative and qualitative performance evaluations on the two above mentioned benchmark datasets demonstrate that this second proposed tracking algorithm generally performs better than several state-of-the-art methods and the first proposed tracking method do. Despite the improved performance of this second proposed tracking algorithm, there are still some challenging attributes of OTB-50 and of VOT2016 for which the performance needs to be improved. Finally, a third tracking algorithm is proposed by developing a scheme for collaboration between the discriminative and generative appearance models. The discriminative model is explored to estimate the position of the target and a new generative model is used to find the remaining affine parameters of the target. In the generative model, robust coding is extended to two dimensions and employed in the bilateral two dimensional PCA (2DPCA) reconstruction procedure to handle the non-Gaussian or non-Laplacian residuals by developing an IRRC technique. A 2D robust coding distance is introduced to differentiate the candidate sample from the one reconstructed from the subspace and used to compute the observation likelihood in the generative model. A method of generating a robust occlusion map from the weights obtained during the IRRC technique and a novel update mechanism of the observation model for both the kernelized correlation filters and the bilateral 2DPCA subspace are developed. Quantitative and qualitative performance evaluations on the two datasets demonstrate that this algorithm with HOG features generally outperforms the state-of-the-art methods and the other two proposed algorithms for most of the challenging attributes.

Visual Object Tracking With Deep Neural Networks

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Author : Pier Luigi Mazzeo
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-12-18

Visual Object Tracking With Deep Neural Networks written by Pier Luigi Mazzeo and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-18 with Computers categories.

Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.

Techniques For Object Tracking

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Author : Pengpeng Liang
language : en
Publisher:
Release Date : 2016

Techniques For Object Tracking written by Pengpeng Liang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Visual object tracking is a fundamental computer vision task, and has a wide range of applications including video surveillance, human computer interaction, augmented reality, vehicle navigation, robotics, etc. In this dissertation, we focus on both developing robust tracking algorithms and creating benchmark datasets for evaluation and diagnosis purposes. First, to comprehensively investigate the effect of encoding color information for the visual tracking task, we develop 160 color-enhanced trackers and compile a dataset containing 128 color sequences for evaluation. We also provide detailed analysis of the results. Second, to deal with the problem that all of the current planar object tracking benchmarks are constructed in laboratory environments, we present a carefully designed planar object tracking benchmark contains 210 video sequences of 30 planar objects sampled in the wild. For each object, we shoot seven videos according to seven challenging factors. We annotate the ground truth in a semi-automatic manner to ensure the accuracy. We also evaluate two representative algorithms and provide detailed analysis of the results. Third, in order to incorporate the reliable prior knowledge that the target object in tracking must be an object other than non-object, we adapt the BING objectness measure to a specific tracking object with adaptive support vector machine. The effectiveness of the proposed adaptive objectness, named ADOBING, is generic. The performance of all the carefully selected base trackers can be improved on two popular benchmarks. Fourth, we propose a blurred target tracking algorithm using group sparse representation which can capture the natural group structure among the templates. Based on the observation that the blur templates of the same direction have similar gradient distributions, we include gradient histograms in the appearance model to further boost the performance. The resulting non-smooth optimization problem is solved with an efficient algorithm based on accelerated proximal gradient scheme. Moving vehicle detection is an important prerequisite for multiple moving vehicle tracking in wide area motion imagery. Based on the motivation that there are usually a relatively large number of vehicles in several consecutive frames along the direction of the road, we present a novel temporal context (TC) feature to capture the road context without detecting road explicitly. We evaluate TC with the CLIF dataset, and the experimental results show that TC is useful to remove false positives which are not on the road.

Online Visual Tracking

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Author : Huchuan Lu
language : en
Publisher: Springer
Release Date : 2019-05-30

Online Visual Tracking written by Huchuan Lu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Computers categories.

This book presents the state of the art in online visual tracking, including the motivations, practical algorithms, and experimental evaluations. Visual tracking remains a highly active area of research in Computer Vision and the performance under complex scenarios has substantially improved, driven by the high demand in connection with real-world applications and the recent advances in machine learning. A large variety of new algorithms have been proposed in the literature over the last two decades, with mixed success. Chapters 1 to 6 introduce readers to tracking methods based on online learning algorithms, including sparse representation, dictionary learning, hashing codes, local model, and model fusion. In Chapter 7, visual tracking is formulated as a foreground/background segmentation problem, and tracking methods based on superpixels and end-to-end deep networks are presented. In turn, Chapters 8 and 9 introduce the cutting-edge tracking methods based on correlation filter and deep learning. Chapter 10 summarizes the book and points out potential future research directions for visual tracking. The book is self-contained and suited for all researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website.

Data Association For Multi Object Visual Tracking

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Author : Margrit Betke
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Data Association For Multi Object Visual Tracking written by Margrit Betke and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

In the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system—data association. Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"—the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers. This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research.

An Exploration Into Model Free Online Visual Object Tracking

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Author : Gao Zhu
language : en
Publisher:
Release Date : 2017

An Exploration Into Model Free Online Visual Object Tracking written by Gao Zhu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.

This thesis presents a thorough investigation of model-free visual object tracking, a fundamental computer vision task that is essential for practical video analytics applications. Given the states of the object in the rst frame, e.g., the position and size of the target, the computational methods developed and advanced in this thesis aim at determining target states in consecutive video frames automatically. In contrast to the tracking schemes that depend strictly on specic object detectors, model-free tracking provides conveniently flexible and competently general solutions where object representations are initiated in the first frame and adapted in an online manner at each frame. We first articulate our motivations and intuitions in Chapter 1, formulate model-free online visual tracking, illustrate outcomes on two representative object tracking applications; drone control and sports video broadcasting analysis, and elaborate other relevant problems. In Chapter 2, we review various tracking methodologies employed by state-ofthe-art trackers and further review related background knowledge, including several important dataset benchmarks and workshop challenges, which are widely used for evaluating the performance of trackers, as well as commonly applied evaluation protocols in this chapter. In Chapter 3 through Chapter 6, we then explore the model-free online visual tracking problem in four different dimensions: 1) learning a more discriminative classier with a two-layer classication hierarchy and background contextual clusters; 2) overcoming the limit of conventionally used local-search scheme with a global object tracking framework based on instance-specic object proposals; 3) tracking object affine motion with a Structured Support Vector Machine (SSVM) framework incorporated with motion manifold structure; 4) an efficient multiple object model-free online tracking approach based on a shared pool of object proposals. Lastly, as a conclusion and future work outlook, we highlight and summarize the contribution of this thesis and discuss several promising research directions in Chapter 7, based on latest work and their drawbacks of current state-of-the-art trackers.

Local Feature Based Representation For Object Tracking

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Author : Feng Tang
language : en
Publisher:
Release Date : 2007

Local Feature Based Representation For Object Tracking written by Feng Tang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Object Tracking Technology

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Author : Ashish Kumar
language : en
Publisher: Springer Nature
Release Date : 2023-10-27

Object Tracking Technology written by Ashish Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-27 with Computers categories.

With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.

Advances In Multimedia Information Processing Pcm 2016

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Author : Enqing Chen
language : en
Publisher: Springer
Release Date : 2016-11-25

Advances In Multimedia Information Processing Pcm 2016 written by Enqing Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-25 with Computers categories.

The two-volume proceedings LNCS 9916 and 9917, constitute the proceedings of the 17th Pacific-Rim Conference on Multimedia, PCM 2016, held in Xi`an, China, in September 2016. The total of 128 papers presented in these proceedings was carefully reviewed and selected from 202 submissions. The focus of the conference was as follows in multimedia content analysis, multimedia signal processing and communications, and multimedia applications and services.

Visual Object Tracking Using Deep Learning

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Author : Ashish Kumar
language : en
Publisher: CRC Press
Release Date : 2023-11-10

Visual Object Tracking Using Deep Learning written by Ashish Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-10 with Technology & Engineering categories.

This book covers the description of both conventional methods and advanced methods. In conventional methods, visual tracking techniques such as stochastic, deterministic, generative, and discriminative are discussed. The conventional techniques are further explored for multi-stage and collaborative frameworks. In advanced methods, various categories of deep learning-based trackers and correlation filter-based trackers are analyzed. The book also: Discusses potential performance metrics used for comparing the efficiency and effectiveness of various visual tracking methods. Elaborates on the salient features of deep learning trackers along with traditional trackers, wherein the handcrafted features are fused to reduce computational complexity. Illustrates various categories of correlation filter-based trackers suitable for superior and efficient performance under tedious tracking scenarios. Explores the future research directions for visual tracking by analyzing the real-time applications. The book comprehensively discusses various deep learning-based tracking architectures along with conventional tracking methods. It covers in-depth analysis of various feature extraction techniques, evaluation metrics and benchmark available for performance evaluation of tracking frameworks. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.