Visual Object Tracking Using Deep Learning
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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 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.
Visual Object Tracking Using Deep Learning
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Author : Ashish Kumar (Analyst)
language : en
Publisher:
Release Date : 2023-10
Visual Object Tracking Using Deep Learning written by Ashish Kumar (Analyst) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10 with Algorithms categories.
"The text comprehensively discusses tracking architecture under stochastic and deterministic frameworks and presents experimental results under each framework with qualitative and quantitative analysis. It covers deep learning techniques for feature extraction, template matching, and training the networks in tracking algorithms"--
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.
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.
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.
Visual Object Recognition
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Author : Kristen Grauman
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011
Visual Object Recognition written by Kristen Grauman and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 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 In Dynamic Scenes
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Author : Mohamed Hamed Abdelpakey
language : en
Publisher:
Release Date : 2021
Visual Object Tracking In Dynamic Scenes written by Mohamed Hamed Abdelpakey 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.
Visual object tracking is a fundamental task in the field computer vision. Visual object tracking is widely used in numerous applications which include, but are not limited to video surveillance, image understanding, robotics, and human-computer interaction. In essence, visual object tracking is the problem of estimating the states/trajectory of the object of interest over time. Unlike other tasks such as object detection where the number of classes/categories are defined beforehand, the only available information of the object of interest is at the first frame. Even though, Deep Learning (DL) has revolutionised most computer vision tasks, visual object tracking still imposes several challenges. The nature of visual object tracking task is stochastic, where no prior-knowledge is available about the object of interest during the training or testing/inference. Moreover, visual object tracking is a class-agnostic task, as opposed object detection and segmentation tasks. In this thesis, the main objective is to develop and advance the visual object trackers using novel designs of deep learning frameworks and mathematical formulations. To take advantage of different trackers, a novel framework is developed to track moving objects based on a composite framework and a reporter mechanism. The composite framework has built-in trackers and user-defined trackers to track the object of interest. The framework contains a module to calculate the robustness for each tracker and a reporter mechanism serves as a recovery mechanism if trackers fail to locate the object of interest. Different trackers may fail to track the object of interest, thus, a more robust framework based on Siamese network architecture, namely DensSiam, is proposed to use the concept of dense layers and connects each dense layer in the network to all layers in a feed-forward fashion with a similarity-learning function. DensSiam also includes a Self-Attention mechanism to force the network to pay more attention to non-local features during offline training. Generally, Siamese trackers do not fully utilize semantic and objectness information from pre-trained networks that have been trained on an image classification task. To solve this problem a novel architecture design is proposed , dubbed DomainSiam, to learn a Domain-Aware that fully utilizes semantic and objectness information while producing a class-agnostic track using a ridge regression network. Moreover, to reduce the sparsity problem, we solve the ridge regression problem with a differentiable weighted-dynamic loss function. Siamese trackers have high speed and work in real-time, however, they lack high accuracy. To overcome this challenge, a novel dynamic policy gradient Agent-Environment architecture with Siamese network (DP-Siam) is proposed to train the tracker to increase the accuracy and the expected average overlap while running in real-time. DP-Siam is trained offline with reinforcement learning to produce a continuous action that predicts the optimal object location. One of the common design block in most object trackers in the literature is the backbone network, where the backbone network is trained in the feature space. To design a backbone network that maps from feature space to another space (i.e., joint-nullspace) and more suitable for object tracking and classification, a novel framework is proposed. The new framework is called NullSpaceNet has a clear interpretation for the feature representation and the features in this space are more separable. NullSpaceNet is utilized in object tracking by regularizing the discriminative joint-nullspace backbone network. The novel tracker is called NullSpaceRDAR, and encourages the network to have a representation for the target-specific information for the object of interest in the joint-nullspace. In contrast to feature space where objects from a specific class are categorized into one category however, it is insensitive to intra-class variations. Furthermore, we use the NullSpaceNet backbone to learn a tracker, dubbed NullSpaceRDAR, with a regularized discriminative joint-nullspace backbone network that is specifically designed for object tracking. In the regularized discriminative joint-nullspace, the features from the same target-specific are collapsed into one point in the joint-null space and different targetspecific features are collapsed into different points in the joint-nullspace. Consequently, the joint-nullspace forces the network to be sensitive to the variations of the object from the same class (intra-class variations). Moreover, a dynamic adaptive loss function is proposed to select the suitable loss function from a super-set family of losses based on the training data to make NullSpaceRDAR more robust to different challenges.