Visual Object Tracking With Deep Neural Networks
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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 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.
Deep Learning In Object Detection And Recognition
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Author : Xiaoyue Jiang
language : en
Publisher: Springer
Release Date : 2020-11-27
Deep Learning In Object Detection And Recognition written by Xiaoyue Jiang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-27 with Computers categories.
This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.
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.
Real World Applications Of Genetic Algorithms
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Author : Olympia Roeva
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-03-07
Real World Applications Of Genetic Algorithms written by Olympia Roeva 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 2012-03-07 with Computers categories.
The book addresses some of the most recent issues, with the theoretical and methodological aspects, of evolutionary multi-objective optimization problems and the various design challenges using different hybrid intelligent approaches. Multi-objective optimization has been available for about two decades, and its application in real-world problems is continuously increasing. Furthermore, many applications function more effectively using a hybrid systems approach. The book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. The book examines various examples of algorithms in different real-world application domains as graph growing problem, speech synthesis, traveling salesman problem, scheduling problems, antenna design, genes design, modeling of chemical and biochemical processes etc.
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.
Deep Learning For Computer Vision
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-04-04
Deep Learning For Computer Vision written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-04 with Computers categories.
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Practical Machine Learning For Computer Vision
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Author : Valliappa Lakshmanan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-07-21
Practical Machine Learning For Computer Vision written by Valliappa Lakshmanan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-21 with Computers categories.
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
On Chip Training Npu Algorithm Architecture And Soc Design
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Author : Donghyeon Han
language : en
Publisher: Springer Nature
Release Date : 2023-08-28
On Chip Training Npu Algorithm Architecture And Soc Design written by Donghyeon Han and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-28 with Technology & Engineering categories.
Unlike most available sources that focus on deep neural network (DNN) inference, this book provides readers with a single-source reference on the needs, requirements, and challenges involved with on-device, DNN training semiconductor and SoC design. The authors include coverage of the trends and history surrounding the development of on-device DNN training, as well as on-device training semiconductors and SoC design examples to facilitate understanding.
Empowering Ai Applications In Smart Life And Environment
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Author : Nour Eldeen Mahmoud Khalifa
language : en
Publisher: Springer Nature
Release Date : 2025-03-28
Empowering Ai Applications In Smart Life And Environment written by Nour Eldeen Mahmoud Khalifa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-28 with Computers categories.
"Empowering AI Applications in Smart Life and Environment" provides a comprehensive exploration of how artificial intelligence (AI) can transform smart environments and contribute to sustainable living. It investigates the integrating of AI with visual, audio, and haptic devices that can revolutionize energy optimization, intelligent transportation, healthcare management, smart farming, and smart homes. The book aims to highlight the latest research and developments in AI applications that drive the enhancement of smart environments and sustainable life. The chapters are divided into two broad parts, the first part of this book discusses "Artificial Intelligence in Smart Systems, Environments and Security" inclusive of, but not limited to AI-based energy efficiency, object detection, defect detection in smart infrastructure, AI-driven IoT platforms, and strategies of machine learning for cybersecurity. The second part entitled "Artificial Intelligence in Smart Healthcare and Sustainability" shows how AI helps in the multi-class diagnosis of skin diseases, elderly care, and enhancement of post-consumer plastics recycling. This book is an intense exercise in learning about the various ways AI can make environments smarter, sustainable, and secure.