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目标跟踪基本原理 fundamentals of object tracking 英文版
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FUNDAMENTALS OF OBJECT TRACKING
Kalman filter, particle filter, IMM, PDA, ITS, random sets ...Thenumberofuseful
object tracking methods is exploding. But how are they related? How do they help
to track everything from aircraft, missiles and extra-terrestrial objects to people and
lymphocyte cells? How can they be adapted to novel applications? Fundamentals
of Object Tracking tells you how.
Starting with the generic object tracking problem, it outlines the generic
Bayesian solution. It then shows systematically how to formulate the major track-
ing problems – maneuvering, multi-object, clutter, out-of-sequence sensors –
within this Bayesian framework and how to derive the standard tracking solutions.
This structured approach makes very complex object tracking algorithms accessi-
ble to the growing number of users working on real-world tracking problems and
supports them in designing their own tracking filters under their unique application
constraints. The book concludes with a chapter on issues critical to the successful
implementation of tracking algorithms, such as track initialization and merging.
SUBHASH CHALLA is a Professor and Senior Principal Researcher at the
NICTA, Victoria Research Laboratory at the University of Melbourne. He is also
the co-founder and CEO of SenSen Networks Pty Ltd, a leading video business
intelligence solutions company.
MARK R. MORELANDE is a Senior Research Fellow in the Melbourne Systems
Laboratory at the University of Melbourne.
DARKO MU
ˇ
SICKI is a Professor in the Department of Electronic Systems Engi-
neering at Hanyang University in Ansan, Republic of Korea.
ROBIN J. EVANS is a Professor of Electrical Engineering at the University of
Melbourne and the Director of NICTA, Victoria Research Laboratory.
William of Ockham
Frustra fit per plura, quod fieri potest per pauciora
(It is vain to do with more what can be done with less)
FUNDAMENTALS OF OBJECT
TRACKING
SUBHASH CHALLA
National ICT Australia (NICTA), University of Melbourne, Australia
MARK R. MORELANDE
University of Melbourne, Australia
DARKO MU
ˇ
SICKI
Hanyang University, Ansan, Republic of Korea
ROBIN J. EVANS
National ICT Australia (NICTA), University of Melbourne, Australia
CAMBRIDGE UNIVERSITY PRESS
Cambridge, New York, Melbourne, Madrid, Cape Town,
Singapore, S˜ao Paulo, Delhi, Tokyo, Mexico City
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by Cambridge University Press, New York
www.cambridge.org
Information on this title: www.cambridge.org/9780521876285
C
S. Challa, M. R. Morelande, D. Mu
ˇ
sicki and R. J. Evans 2011
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2011
Printed in the United Kingdom at the University Press, Cambridge
A catalogue record for this publication is available from the British Library
Library of Congress Cataloging in Publication data
Fundamentals of object tracking / Subhash Challa...[etal.].
p. cm.
Includes index.
ISBN 978-0-521-87628-5 (hardback)
1. Linear programming. 2. Programming (Mathematics) I. Challa, Sudha, 1953–
QA402.5.F86 2011
519.7 – dc22 2011008595
ISBN 978-0-521-87628-5 Hardback
Cambridge University Press has no responsibility for the persistence or
accuracy of URLs for external or third-party internet websites referred to
in this publication, and does not guarantee that any content on such
websites is, or will remain, accurate or appropriate.
Contents
Preface page ix
1 Introduction to object tracking 1
1.1 Overview of object tracking problems 2
1.2 Bayesian reasoning with application to object tracking 7
1.3 Recursive Bayesian solution for object tracking 16
1.4 Summary 21
2 Filtering theory and non-maneuvering object tracking 22
2.1 The optimal Bayesian filter 22
2.2 The Kalman filter 25
2.3 The extended Kalman filter 31
2.4 The unscented Kalman filter 36
2.5 The point mass filter 43
2.6 The particle filter 46
2.7 Performance bounds 53
2.8 Illustrative example 57
2.9 Summary 60
3 Maneuvering object tracking 62
3.1 Modeling for maneuvering object tracking 62
3.2 The optimal Bayesian filter 66
3.3 Generalized pseudo-Bayesian filters 72
3.4 Interacting multiple model filter 84
3.5 Particle filters for maneuvering object tracking 91
3.6 Performance bounds 97
3.7 Illustrative example 99
3.8 Summary 102
v
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