Debarati Bhunia Chakraborty - Granular Video Computing: With Rough Sets, Deep Learning And In Iot

Granular Video
Computing

with Rough Sets,
Deep Learning and in IoT

Granular Video
Computing

with Rough Sets,
Deep Learning and in IoT

Debarati B Chakraborty

Indian Institute of Technology Jodhpur, India

Sankar K Pal

Indian Statistical Institute Kolkata, India

Published by

World Scientific Publishing Co. Pte. Ltd.

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Library of Congress Cataloging-in-Publication Data

Names: Chakraborty, Debarati B, author. | Pal, Sankar K., author.

Title: Granular video computing : with rough sets, deep learning and in IoT / Debarati B Chakraborty, Indian Institute of Technology, Jodhpur, India, Sankar K Pal, Indian Statistical Institute, Kolkata, India.

Description: Singapore ; New Jersey : World Scientific Publishing Co. Pte. Ltd., [2021] | Includes bibliographical references and index.

Identifiers: LCCN 2020040374 | ISBN 9789811227110 (hardcover) | ISBN 9789811227127 (ebook) | ISBN 9789811227134 (ebook other)

Subjects: LCSH: Computer vision. | Automatic tracking.

Classification: LCC TA1634 .C435 2021 | DDC 006.7--dc23

LC record available at https://lccn.loc.gov/2020040374

British Library Cataloguing-in-Publication Data

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Preface

The volume Granular Video Computing with Rough Sets, Deep Learning and in IoTis an outcome of the investigations initiated by the authors in 2011 as a part of granular computing research at the Center for Soft Computing Research (CSCR), a national facility, at the Indian Statistical Institute (ISI), Kolkata. The center was established in 2005 by the Department of Science and Technology, Govt. of India (with Prof. Sankar K. Pal as the Principal Investigator) under its prestigious IRHPA (Intensification of Research in High Priority Areas) program. It is now an Affiliated Institute of ISI since 2010.

Video computing is one of the basic steps in humancomputer interactive (HCI) systems. Its primary applications lie in automated surveillance, gesture recognition, event detection, behavior recognition and internet of things (IoT). The steps of video computing involve tracking of moving objects, motion analysis and video interpretation. There are several unsolved issues in the task of video computing. These include handling uncertainties arising from different types of ambiguities in video sequences. Ambiguity may result, for example, from the absence of complete information regarding the number of moving object(s), background, newly appeared object(s), moving-to-static object(s), partly moving object(s), partially occluded or overlapped object(s) and fully occluded or overlapped moving object(s). Accuracy and speed in object recognition and video understanding, and performing these tasks in the absence of large labeled dataset are some other issues of concern.