Granular Video Computing: With Rough Sets, Deep Learning And In Iot

This book explores the use of granular computing with deep learning and the Internet of Things for video analysis, addressing object tracking, occlusion/overlapping handling, reliability, recognition, and event prediction. It is suitable for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology.

\n Format: Hardback
\n Length: 256 pages
\n Publication date: 03 March 2021
\n Publisher: World Scientific Publishing Co Pte Ltd
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This comprehensive volume explores the intersection of granular computing, leveraging deep learning techniques, and the Internet of Things (IoT) for effective object tracking in video analysis. It delves into the challenges associated with video processing, such as handling uncertainties within rough set theoretic granular computing frameworks. The book addresses a range of topics, including object tracking in constrained environments, managing occlusion and overlapping issues, assessing the reliability of tracking methods, recognizing objects in video scenes, and interpreting linguistic content. Additionally, it explores event prediction from videos and explores strategies to minimize data dependency in unsupervised training scenarios.

Designed as a textbook and reference resource for advanced students and researchers in computer science, electrical engineering, system science, data science, and information technology, this book is invaluable for both students and practitioners engaged in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.

The book is organized into five chapters, each covering a distinct aspect of granular computing and object tracking in video analysis. Chapter 1 provides an introduction to granular computing and its applications in video analysis. Chapter 2 explores the theoretical foundations of rough set theory and its relevance to granular computing. Chapter 3 discusses the use of deep learning for object tracking in video analysis. Chapter 4 presents various granular computing frameworks for handling uncertainties in video processing. Chapter 5 showcases real-world applications of granular computing and object tracking in video analysis, including sports analytics, surveillance systems, and autonomous vehicles.

Throughout the book, practical examples and case studies are provided to illustrate the theoretical concepts and demonstrate the effectiveness of granular computing and object tracking in video analysis. The book also includes an extensive bibliography and a list of recommended resources for further study.

In summary, this volume offers a comprehensive and up-to-date exploration of granular computing and object tracking in video analysis, providing valuable insights and techniques for advanced students, researchers, and practitioners in the field.

\n Weight: 516g\n
Dimension: 159 x 236 x 22 (mm)\n
ISBN-13: 9789811227110\n \n