Download Free Online Visual Tracking Book in PDF and EPUB Free Download. You can read online Online Visual Tracking and write the review.

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.
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.
Eyetracking Web Usability is based on one of the largest studies of eyetracking usability in existence. Best-selling author Jakob Nielsen and coauthor Kara Pernice used rigorous usability methodology and eyetracking technology to analyze 1.5 million instances where users look at Web sites to understand how the human eyes interact with design. Their findings will help designers, software developers, writers, editors, product managers, and advertisers understand what people see or don’t see, when they look, and why. With their comprehensive three-year study, the authors confirmed many known Web design conventions and the book provides additional insights on those standards. They also discovered important new user behaviors that are revealed here for the first time. Using compelling eye gaze plots and heat maps, Nielsen and Pernice guide the reader through hundreds of examples of eye movements, demonstrating why some designs work and others don’t. They also provide valuable advice for page layout, navigation menus, site elements, image selection, and advertising. This book is essential reading for anyone who is serious about doing business on the Web.
An online neutrosophic similarity-based objectness tracking with a weighted multiple instance learning algorithm (NeutWMIL) is proposed. Each training sample is extracted surrounding the object location, and the distribution of these samples is symmetric. To provide a more robust weight for each sample in the positive bag, the asymmetry of the importance of the samples is considered. The neutrosophic similarity-based objectness estimation with object properties (super straddling) is applied.
Digital classrooms have become a common addition to curriculums in higher education; however, such learning systems are only successful if students are properly motivated to learn. Optimizing Student Engagement in Online Learning Environments is a critical scholarly resource that examines the importance of motivation in digital classrooms and outlines methods to reengage learners. Featuring coverage on a broad range of topics such as motivational strategies, learning assessment, and student involvement, this book is geared toward academicians, researchers, and students seeking current research on the importance of maintaining ambition among learners in digital classrooms.
This book discusses research, methods, and recent developments in the interdisciplinary field that spans research in visualization, eye tracking, human-computer interaction, and psychology. It presents extended versions of papers from the First Workshop on Eye Tracking and Visualization (ETVIS), which was organized as a workshop of the IEEE VIS Conference 2015. Topics include visualization and visual analytics of eye-tracking data, metrics and cognitive models, eye-tracking experiments in the context of visualization interfaces, and eye tracking in 3D and immersive environments. The extended ETVIS papers are complemented by a chapter offering an overview of visualization approaches for analyzing eye-tracking data and a chapter that discusses electrooculography (EOG) as an alternative of acquiring information about eye movements. Covering scientific visualization, information visualization, and visual analytics, this book is a valuable resource for eye-tracking researchers within the visualization community.
Eye tracking is a widely used research method, but there are many questions and misconceptions about how to effectively apply it. Eye Tracking the User Experience—the first how-to book about eye tracking for UX practitioners—offers step-by-step advice on how to plan, prepare, and conduct eye tracking studies; how to analyze and interpret eye movement data; and how to successfully communicate eye tracking findings.
Eye-tracking is quickly becoming a valuable tool in applied linguistics research as it provides a 'real-time', direct measure of cognitive processing effort. This book provides a straightforward introduction to the technology and how it might be used in language research. With a strong focus on the practicalities of designing eye-tracking studies that achieve the standard of other well-established experimental techniques, it provides valuable information about building and designing studies, touching on common challenges and problems, as well as solutions. Importantly, the book looks at the use of eye-tracking in a wide variety of applied contexts including reading, listening and multi-modal input, writing, testing, corpus linguistics, translation, stylistics, and computer-mediated communication. Each chapter finishes with a simple checklist to help researchers use eye-tracking in a wide variety of language studies. Discussion is grounded in concrete examples, which will allow users coming to the technology for the first time to gain the knowledge and confidence to use it to produce high quality research.