Machine Learning for Vision-Based Motion Analysis Theory and Techniq
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfa
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Liang Wang Guoying Zhao Li Cheng Matti Pietikäinen Editors
Machine Learning for Vision-Based Motion Analysis Theory and Techniques
Editors Dr. Liang Wang Department of Computer Science University of Bath Bath, BA2 7AY UK [email protected]
Dr. Guoying Zhao Department of Electrical and Information Engineering University of Oulu Oulu Finland [email protected]
Dr. Li Cheng Bioinformatics Institute A*STAR 30 Biopolis Street, #07-01 Matrix Singapore, 138671 Singapore [email protected] Dr. Matti Pietikäinen Department of Electrical Engineering, Machine Vision & Media Processing Unit University of Oulu Oulu Finland [email protected]
Series Editor Professor Sameer Singh, PhD Research School of Informatics Loughborough University Loughborough UK
ISSN 1617-7916 ISBN 978-0-85729-056-4 e-ISBN 978-0-85729-057-1 DOI 10.1007/978-0-85729-057-1 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library © Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: VTEX, Vilnius Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Vision-based motion analysis aims to detect, track and identify objects, and more generally, to understand their behaviors, from image sequences. With the ubiquitous presence of video data and its increasing importance in a wide range of real-world applications such as visual surveillance, human-machine interfaces and sport event interpretation, it is becoming increasingly important to automatically analyze and understand object motions from large amount of video footage. Not surprisingly, this exciting research area has received growing interest in recent years. Although there has been much progress in the past decades, many challenging problems remain unsolved, e.g., robust object detection and tracking, unconstrained object activity recognition, etc. Recently, statistical machine learning
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