An Exploration Into Model Free Online Visual Object Tracking

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An Exploration Into Model-Free Online Visual Object Tracking

Author : Gao Zhu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)

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Book Synopsis An Exploration Into Model-Free Online Visual Object Tracking by : Gao Zhu

Download or read book An Exploration Into Model-Free Online Visual Object Tracking written by Gao Zhu and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Visual Object Tracking from Correlation Filter to Deep Learning

Author : Weiwei Xing
Publisher : Springer Nature
ISBN 13 : 9811662428
Total Pages : 202 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Visual Object Tracking from Correlation Filter to Deep Learning by : Weiwei Xing

Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by Springer Nature. This book was released on 2021-11-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.

Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments

Author : Salma Moujtahid
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments by : Salma Moujtahid

Download or read book Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments written by Salma Moujtahid and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing need for automated video analysis, visual object tracking became an important task in computer vision. Object tracking is used in a wide range of applications such as surveillance, human-computer interaction, medical imaging or vehicle navigation. A tracking algorithm in unconstrained environments faces multiple challenges : potential changes in object shape and background, lighting, camera motion, and other adverse acquisition conditions. In this setting, classic methods of background subtraction are inadequate, and more discriminative methods of object detection are needed. Moreover, in generic tracking algorithms, the nature of the object is not known a priori. Thus, off-line learned appearance models for specific types of objects such as faces, or pedestrians can not be used. Further, the recent evolution of powerful machine learning techniques enabled the development of new tracking methods that learn the object appearance in an online manner and adapt to the varying constraints in real time, leading to very robust tracking algorithms that can operate in non-stationary environments to some extent. In this thesis, we start from the observation that different tracking algorithms have different strengths and weaknesses depending on the context. To overcome the varying challenges, we show that combining multiple modalities and tracking algorithms can considerably improve the overall tracking performance in unconstrained environments. More concretely, we first introduced a new tracker selection framework using a spatial and temporal coherence criterion. In this algorithm, multiple independent trackers are combined in a parallel manner, each of them using low-level features based on different complementary visual aspects like colour, texture and shape. By recurrently selecting the most suitable tracker, the overall system can switch rapidly between different tracking algorithms with specific appearance models depending on the changes in the video. In the second contribution, the scene context is introduced to the tracker selection. We designed effective visual features, extracted from the scene context to characterise the different image conditions and variations. At each point in time, a classifier is trained based on these features to predict the tracker that will perform best under the given scene conditions. We further improved this context-based framework and proposed an extended version, where the individual trackers are changed and the classifier training is optimised. Finally, we started exploring one interesting perspective that is the use of a Convolutional Neural Network to automatically learn to extract these scene features directly from the input image and predict the most suitable tracker.

Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments

Author : Salma Moujtahid
Publisher :
ISBN 13 :
Total Pages : 137 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments by : Salma Moujtahid

Download or read book Exploiting Scene Context for On-line Object Tracking in Unconstrained Environments written by Salma Moujtahid and published by . This book was released on 2019 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing need for automated video analysis, visual object tracking became an important task in computer vision. Object tracking is used in a wide range of applications such as surveillance, human-computer interaction, medical imaging or vehicle navigation. A tracking algorithm in unconstrained environments faces multiple challenges : potential changes in object shape and background, lighting, camera motion, and other adverse acquisition conditions. In this setting, classic methods of background subtraction are inadequate, and more discriminative methods of object detection are needed. Moreover, in generic tracking algorithms, the nature of the object is not known a priori. Thus, off-line learned appearance models for specific types of objects such as faces, or pedestrians can not be used. Further, the recent evolution of powerful machine learning techniques enabled the development of new tracking methods that learn the object appearance in an online manner and adapt to the varying constraints in real time, leading to very robust tracking algorithms that can operate in non-stationary environments to some extent. In this thesis, we start from the observation that different tracking algorithms have different strengths and weaknesses depending on the context. To overcome the varying challenges, we show that combining multiple modalities and tracking algorithms can considerably improve the overall tracking performance in unconstrained environments. More concretely, we first introduced a new tracker selection framework using a spatial and temporal coherence criterion. In this algorithm, multiple independent trackers are combined in a parallel manner, each of them using low-level features based on different complementary visual aspects like colour, texture and shape. By recurrently selecting the most suitable tracker, the overall system can switch rapidly between different tracking algorithms with specific appearance models depending on the changes in the video. In the second contribution, the scene context is introduced to the tracker selection. We designed effective visual features, extracted from the scene context to characterise the different image conditions and variations. At each point in time, a classifier is trained based on these features to predict the tracker that will perform best under the given scene conditions. We further improved this context-based framework and proposed an extended version, where the individual trackers are changed and the classifier training is optimised. Finally, we started exploring one interesting perspective that is the use of a Convolutional Neural Network to automatically learn to extract these scene features directly from the input image and predict the most suitable tracker.

Online Visual Tracking

Author : Huchuan Lu
Publisher : Springer
ISBN 13 : 9789811304682
Total Pages : 128 pages
Book Rating : 4.3/5 (46 download)

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Book Synopsis Online Visual Tracking by : Huchuan Lu

Download or read book Online Visual Tracking written by Huchuan Lu and published by Springer. This book was released on 2019-08-09 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Visual Object Tracking with Deep Neural Networks

Author : Pier Luigi Mazzeo
Publisher : BoD – Books on Demand
ISBN 13 : 1789851572
Total Pages : 208 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Visual Object Tracking with Deep Neural Networks by : Pier Luigi Mazzeo

Download or read book Visual Object Tracking with Deep Neural Networks written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2019-12-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Visual Object Tracking using Deep Learning

Author : Ashish Kumar
Publisher : CRC Press
ISBN 13 : 1000991008
Total Pages : 248 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Visual Object Tracking using Deep Learning by : Ashish Kumar

Download or read book Visual Object Tracking using Deep Learning written by Ashish Kumar and published by CRC Press. This book was released on 2023-11-10 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

RiTA 2020

Author : Esyin Chew
Publisher : Springer Nature
ISBN 13 : 9811648034
Total Pages : 443 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis RiTA 2020 by : Esyin Chew

Download or read book RiTA 2020 written by Esyin Chew and published by Springer Nature. This book was released on 2021-09-05 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the Proceedings of the 8th International Conference on Robot Intelligence Technology and Applications (RITA 2020). The areas covered include: Instrumentation and Control, Automation, Autonomous Systems, Biomechatronics and Rehabilitation Engineering, Intelligent Systems, Machine Learning, Mobile Robotics, Social Robotics and Humanoid Robotics, Sensors and Actuators, and Machine Vision, as well as Signal and Image Processing. As a valuable asset, the book offers researchers and practitioners a timely overview of the latest advances in robot intelligence technology and its applications.

Computer Vision – ACCV 2016

Author : Shang-Hong Lai
Publisher : Springer
ISBN 13 : 3319541846
Total Pages : 442 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Computer Vision – ACCV 2016 by : Shang-Hong Lai

Download or read book Computer Vision – ACCV 2016 written by Shang-Hong Lai and published by Springer. This book was released on 2017-03-09 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 10111-10115 constitutes the thoroughly refereed post-conference proceedings of the 13th Asian Conference on Computer Vision, ACCV 2016, held in Taipei, Taiwan, in November 2016. The total of 143 contributions presented in these volumes was carefully reviewed and selected from 479 submissions. The papers are organized in topical sections on Segmentation and Classification; Segmentation and Semantic Segmentation; Dictionary Learning, Retrieval, and Clustering; Deep Learning; People Tracking and Action Recognition; People and Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision.

Model-based Visual Tracking

Author : Giorgio Panin
Publisher : Wiley
ISBN 13 : 9780470876138
Total Pages : 318 pages
Book Rating : 4.8/5 (761 download)

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Book Synopsis Model-based Visual Tracking by : Giorgio Panin

Download or read book Model-based Visual Tracking written by Giorgio Panin and published by Wiley. This book was released on 2011-05-31 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Monocular Model-based 3D Tracking of Rigid Objects

Author : Vincent Lepetit
Publisher : Now Publishers Inc
ISBN 13 : 9781933019031
Total Pages : 108 pages
Book Rating : 4.0/5 (19 download)

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Book Synopsis Monocular Model-based 3D Tracking of Rigid Objects by : Vincent Lepetit

Download or read book Monocular Model-based 3D Tracking of Rigid Objects written by Vincent Lepetit and published by Now Publishers Inc. This book was released on 2005 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monocular Model-Based 3D Tracking of Rigid Objects reviews the different techniques and approaches that have been developed by industry and research.

Computer Vision

Author : Md Atiqur Rahman Ahad
Publisher : CRC Press
ISBN 13 : 104002937X
Total Pages : 359 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Computer Vision by : Md Atiqur Rahman Ahad

Download or read book Computer Vision written by Md Atiqur Rahman Ahad and published by CRC Press. This book was released on 2024-07-30 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications. This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI. Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and providing a range of perspectives from top researchers around the world. A substantial compilation of ideas and state-of-the-art solutions, it will be of great benefit to students, researchers, and industry practitioners.

Mining Intelligence and Knowledge Exploration

Author : Seifedine Kadry
Publisher : Springer Nature
ISBN 13 : 3031440846
Total Pages : 440 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Mining Intelligence and Knowledge Exploration by : Seifedine Kadry

Download or read book Mining Intelligence and Knowledge Exploration written by Seifedine Kadry and published by Springer Nature. This book was released on 2023-09-23 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023, held in Kristiansand, Norway, during June 28–30, 2023. The 22 full papers and 16 short papers included in this book were carefully reviewed and selected from 87 submissions. They were grouped into various subtopics including Knowledge Exploration in IoT, Medical Informatics, Machine Learning, Text Mining, Natural Language Processing, Cryptocurrency and Blockchain, Application of Artificial Intelligence, and other areas.

A Hybrid Model Approach for Real-time Visual Object Tracking

Author : Jinwei Yuan
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (97 download)

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Book Synopsis A Hybrid Model Approach for Real-time Visual Object Tracking by : Jinwei Yuan

Download or read book A Hybrid Model Approach for Real-time Visual Object Tracking written by Jinwei Yuan and published by . This book was released on 2016 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking an unknown general object in video sequences is a challenging task in many computer vision applications since the appearance of the object can change significantly due to pose variations, illumination changes, shape deformations, and abrupt motions. In this dissertation, we address these object tracking challenges by building a hybrid model tracker which consists of a tracking by detection framework and a Kalman filtering framework. We first present two tracking by detection approaches by applying the naive Bayes classifier and the support vector machine approach respectively. In both approaches, effective feature selection schemes are proposed to select the most informative features to construct a robust appearance model. Then, we propose a novel approach for appearance estimation in object tracking. Most existing tracking algorithms assume that the object appearance is static for two consecutive frames, which remains a potential risk causing the drift problem. We build a Kalman filtering framework to generate a statistically optimal estimation for the object appearance. The proposed method greatly reduces the error between the true object appearance and the estimated object appearance, thus effectively improving the tracking performance. Finally, we develop and implement a hybrid model tracking system which combines the discriminative model constructed in the tracking by detection framework and the generative model estimated by the Kalman filtering framework. The support vector machine tracker is applied to provide accurate feedback to the Kalman filtering framework, which improves the estimating precision. The Kalman filtering framework then generates the optimal estimation of the object appearance and determines the object location by the best fitting with the appearance model. The object location determined by maximizing the classifier confidence in the support vector tracking framework is finally corrected by the Kalman filtering framework. Therefore, the proposed tracking system provides more meaningful tracking results compared with traditional tracking by detection algorithms which suffer from the inconsistent objectives between tracking and classification. Our experimental evaluations show that a significant improvement over state-of-the-art methods is achieved by our approach.

Computer Vision -- ECCV 2014

Author : David Fleet
Publisher : Springer
ISBN 13 : 3319105841
Total Pages : 656 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Computer Vision -- ECCV 2014 by : David Fleet

Download or read book Computer Vision -- ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.

Appliable Linguistics and Social Semiotics

Author : David Caldwell
Publisher : Bloomsbury Publishing
ISBN 13 : 1350109312
Total Pages : 545 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Appliable Linguistics and Social Semiotics by : David Caldwell

Download or read book Appliable Linguistics and Social Semiotics written by David Caldwell and published by Bloomsbury Publishing. This book was released on 2022-10-06 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exploring the relationship between theory and practice in Systemic Functional Linguistics (SFL), this volume offers a state-of-the-art overview of Appliable Linguistics. Featuring both internationally-renowned scholars and rising stars from Argentina, Australia, Austria, Brazil, Chile, Denmark, Indonesia, New Zealand, Singapore and the USA, Appliable Linguistics and Social Semiotics examines the theoretical insights, questions, and developments that have emerged from the application of Systemic Functional theory to a range of fields. Beyond simply reporting on the application of SFL to particular sites of communication, both linguistic and semiotic, this volume demonstrates how SFL has critiqued, developed and transformed theory and practice and foregrounds the implications of application for Systemic Functional theory itself. Covering established fields for application, such as education, medicine and media, to relatively uncharted areas, such as software design and extremist propaganda, this volume provides an overview of recent linguistic and semiotic innovations informed by SFL and examines the advances that have been made from many years of productive dialogue between theory and practice.

Mining Intelligence and Knowledge Exploration

Author : Richard Chbeir
Publisher : Springer Nature
ISBN 13 : 3031215176
Total Pages : 248 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Mining Intelligence and Knowledge Exploration by : Richard Chbeir

Download or read book Mining Intelligence and Knowledge Exploration written by Richard Chbeir and published by Springer Nature. This book was released on 2022-12-14 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the refereed proceedings of the 9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021, which took place in Hammamet, Tunisia, in November 2021. The 22 full papers included in this book were carefully reviewed and selected from 61 submissions. They deal with topics such as evolutionary computation, knowledge exploration in IoT, artificial intelligence, machine learning, data mining and information retrieval, medical image analysis, pattern recognition and computer vision, speech / signal processing, text mining and natural language processing, intelligent security systems, Smart and Intelligent Systems, etc.