This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training. This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing. Key Features: o Linking the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis o Is structured according to the major challenges of video processing (e.g., tracking, occlusion handling, reliability measure, scene recognition, and real-time event prediction in IoT) while defining new variations of rough sets and granular computing developed depending on the type of problems o Provides a balanced mixture between theory, algorithms and applications

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