Human Activity Recognition and Prediction
This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained
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Human Activity Recognition and Prediction
Human Activity Recognition and Prediction
Yun Fu Editor
Human Activity Recognition and Prediction
123
Editor Yun Fu Northeastern University Boston, Massachusetts, USA
ISBN 978-3-319-27002-9 ISBN 978-3-319-27004-3 (eBook) DOI 10.1007/978-3-319-27004-3 Library of Congress Control Number: 2015959271 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
Preface
Automatic human activity sensing has drawn much attention in the field of video analysis technology due to the growing demands from many applications, such as surveillance environments, entertainments, and healthcare systems. Human activity recognition and prediction is closely related to other computer vision tasks such as human gesture analysis, gait recognition, and event recognition. Very recently, the US government funded many major research projects on this topic; in industry, commercial products such as the Microsoft’s Kinect are good examples that make use of human action recognition techniques. Many commercialized surveillance systems seek to develop and exploit video-based detection, tracking and activity recognition of persons, and vehicles in order to infer their threat potential and provide automated alerts. This book focuses on the recognition, prediction of individual activities and interactions from videos that usually involves several people. This provides a unique view of: human activity recognition, especially fine-grained human activity structure learning, human interaction recognition, RGB-D data-based recognition temporal decomposition, and casually learning in unconstrained human activity videos. These techniques will significantly advance existing methodologies of video content understanding by taking advantage o
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