Visual Object Tracking Using Deep Learning
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Visual Object Tracking With Deep Neural Networks
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Author :
language : en
Publisher:
Release Date : 2019
Visual Object Tracking With Deep Neural Networks written by 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 Object Tracking Using Deep Learning
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Author : Ashish Kumar
language : en
Publisher: CRC Press
Release Date : 2023-11-20
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 2023-11-20 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.
Visual Object Tracking From Correlation Filter To Deep Learning
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Author : Weiwei Xing
language : en
Publisher: Springer Nature
Release Date : 2021-11-18
Visual Object Tracking From Correlation Filter To Deep Learning written by Weiwei Xing and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-18 with Computers categories.
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.
Visual Object Tracking From Correlation Filter To Deep Learning
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Author : Weiwei Xing
language : en
Publisher:
Release Date : 2021
Visual Object Tracking From Correlation Filter To Deep Learning written by Weiwei Xing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
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.
Robust And Accurate Generic Visual Object Tracking Using Deep Neural Networks In Unconstrained Environments
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Author : Javad Khaghani
language : en
Publisher:
Release Date : 2021
Robust And Accurate Generic Visual Object Tracking Using Deep Neural Networks In Unconstrained Environments written by Javad Khaghani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Automatic tracking categories.
The availability of affordable cameras and video-sharing platforms have provided a massive amount of low-cost videos. Automatic tracking of objects of interest in these videos is the essential step for complex visual analyses. As a fundamental computer vision task, Visual Object Tracking aims at accurately (and efficiently) locating a target in an arbitrary video, given an initial bounding box in the first frame. While the state-of-the-art deep trackers provide promising results, they still suffer from performance degradation in challenging scenarios including small targets, occlusion, and viewpoint change. Also, estimating the axis-aligned bounding box enclosing the target cannot provide the full details about its boundaries. Moreover, the performance of tracker relies on its well-crafted modules, typically consisting of manually-designed network architectures to boost the performance. In this thesis, first, a context-aware IoU-guided tracker is proposed that exploits a multitask two-stream network and an offline reference proposal generation strategy to improve the accuracy for tracking class-agnostic small objects from aerial videos of medium to high altitudes. Then, a two-stage segmentation tracker to provide better semantically interpretation of target in videos is developed. Finally, a novel cell-level differentiable architecture search with early stopping is introduced into Siamese tracking framework to automate the network design of the tracking module, aiming to adapt backbone features to the objective of network. Extensive experimental evaluations on widely used generic and aerial visual tracking benchmarks demonstrate the effectiveness of the proposed methods.
Visual Object Tracking Across Modalities
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Author : Mengmeng Wang
language : en
Publisher: Springer
Release Date : 2025-12-03
Visual Object Tracking Across Modalities written by Mengmeng Wang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-03 with Computers categories.
Discover the cutting-edge advancements in visual object tracking (VOT) with this comprehensive resource, designed to revolutionize how researchers and professionals approach tracking systems. This book presents deep learning techniques and multimodal fusion strategies, offering state-of-the-art solutions for robust and accurate object tracking in dynamic environments. With applications ranging from autonomous vehicles to intelligent surveillance, VOT has become a cornerstone of modern computer vision. By addressing challenges like scalability, real-time performance, and robustness, this book equips readers with the tools to navigate the rapidly evolving landscape of tracking systems. It’s the first of its kind to seamlessly integrate single-modal and multimodal approaches, bridging the gap between foundational methods and emerging technologies. Explore key topics including Siamese networks, transformer-based models, RGB-LiDAR and RGB-thermal fusion, and spatio-temporal modeling. Gain insights into benchmark datasets, evaluation protocols, and future trends like large model transfer and cross-domain learning. Each chapter builds on the next, ensuring a structured progression from theoretical principles to practical applications. Whether you’re a researcher, practitioner, or student in computer vision, artificial intelligence, or machine learning, this book is an indispensable guide to mastering VOT. A basic understanding of computer science and deep learning concepts is recommended to fully benefit from the material.
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.
Visual Object Recognition
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Author : Kristen Grauman
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Visual Object Recognition written by Kristen Grauman 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.
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Visual Object Tracking
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Author : Xin Zhao
language : en
Publisher: Springer Nature
Release Date : 2025-07-01
Visual Object Tracking written by Xin Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.
This book delves into visual object tracking (VOT), a fundamental aspect of computer vision crucial for replicating human dynamic vision, with applications ranging from self-driving vehicles to surveillance systems. Despite significant strides propelled by deep learning, challenges such as target deformation and motion persist, exposing a disparity between cutting-edge VOT systems and human performance. This observation underscores the necessity to thoroughly scrutinize and enhance evaluation methodologies within VOT research. Hence, the primary objective of this book is to equip readers with essential insights into dynamic visual tasks encapsulated by VOT. Beginning with the elucidation of task definitions, it integrates interdisciplinary perspectives on evaluation techniques. The book is organized into five parts, tracing the evolution of VOT from perceptual to cognitive intelligence, exploring the experimental frameworks utilized in assessments, analyzing the various agents involved, including tracking algorithms and human visual tracking, and dissecting evaluation mechanisms through both machine–machine and human–machine comparisons. Furthermore, it examines the trend toward crafting more human-like task definitions and comprehensive evaluation frameworks to effectively gauge machine intelligence. This book serves as a roadmap for researchers aiming to grasp the bottlenecks in VOT capabilities and comprehend the gaps between current methodologies and human abilities, all geared toward advancing algorithmic intelligence. It also delves into the realm of data-centric AI, emphasizing the pivotal role of high-quality datasets and evaluation systems in the age of large language models (LLMs). Such systems are indispensable for training AI models while ensuring their safety and reliability. Utilizing VOT as a case study, the book offers detailed insights into these facets of data-centric AI research. Designed to cater to readers with foundational knowledge in computer vision, it employs diagrams and examples to facilitate comprehension, providing essential groundwork for understanding key technical components.
Information Extraction And Object Tracking In Digital Video
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2022-08-17
Information Extraction And Object Tracking In Digital Video written by 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 2022-08-17 with Computers categories.
The research on computer vision systems has been increasing every day and has led to the design of multiple types of these systems with innumerous applications in our daily life. The recent advances in artificial intelligence, together with the huge amount of digital visual data now available, have boosted vision system performance in several ways. Information extraction and visual object tracking are essential tasks in the field of computer vision with a huge number of real-world applications.This book is a result of research done by several researchers and professionals who have highly contributed to the field of image processing. It contains eight chapters divided into three sections. Section 1 consists of four chapters focusing on the problem of visual tracking. Section 2 includes three chapters focusing on information extraction from images. Finally, Section 3 includes one chapter that presents new advances in image sensors.