Fundamentals of Object Tracking / Subhash Challa, Mark R. Morelande, Darko Mušicki, Robin J. Evans.

Format:

Book

Contributor:

Challa, Subhash.

Morelande, Mark R. .

Mušicki, Darko.

Evans, Robin J.

Series:

Cambridge books online.

Language:

English

Subjects (All):

Linear programming.

Programming (Mathematics).

Physical Description:

1 online resource (392 pages) : digital, PDF file(s)

Place of Publication:

Cambridge : Cambridge University Press, 2011.

System Details:

Mode of access: World Wide Web.

text file

PDF

Summary:

Kalman filter, particle filter, IMM, PDA, ITS, random sets... The number of useful 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 tracking 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 accessible 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. Book jacket.

Robin J. Evans is a Professor of Electrical Engineering at the University of Melbourne and the Director of NICTA, Victoria Research Laboratory. Book jacket.

Contents:

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 1 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

4 Single-object tracking in clutter 103

4.1 The optimal Bayesian filter 104

4.2 The nearest neighbor filter 107

4.3 The probabilistic data association filter 111

4.4 Maneuvering object tracking in clutter 119

4.5 Particle filter for tracking in clutter 122

4.6 Performance bounds 126

4.7 Illustrative examples 131

4.8 Summary 132

5 Single- and multiple-object tracking in clutter: object-existence-based approach 133

5.1 Introduction 133

.5.2 Problem statement/models 138

5.3 Track state 142

5.4 Optimal Bayes' recursion 147

5.5 Optimal track update cycle 171

5.6 Track component control 184

5.7 Object-existence-based single-object tracking 191

5.8 Object-existence-based multi-object tracking 205

5.9 Summary 221

6 Multiple-object tracking in clutter: random-set-based approach 223

6.1 The optimal Bayesian multi-object tracking filter 225

6.2 The probabilistic hypothesis density approximations 227

6.3 Approximate filters 237

6.4 Object-existence-based tracking filters 244

6.5 Performance bounds 260

6.6 Illustrative example 262

6.7 Summary 264

7 Bayesian smoothing algorithms for object tracking 265

7.1 Introduction to smoothing 265

7.2 Optimal Bayesian smoothing 266

7.3 Augmented state Kalman smoothing 268

7.4 Smoothing for maneuvering object tracking 271

7.5 Smoothing for object tracking in clutter 275

7.6 Smoothing with object existence uncertainty 278

7.7 Illustrative example 283

7.8 Summary 288

8 Object tracking with time-delayed, out-of-sequence measurements 289

8.1 Optimal Bayesian solution to the OOSM problem 289

8.2 Single- and multi-lag OOSM algorithms 293

8.3 Augmented state Kalman filter for multiple-lag OOSM 294

8.4 Augmented state PDA filter for multiple-lag OOSM in clutter 297

8.5 Simulation results 302

8.6 Summary 311

9 Practical object tracking 312

9.1 Introduction 312

9.2 Linear multi-target tracking 313

9.3 Clutter measurement density estimation 317

9.4 Track initialization 322

9.5 Track merging 329

9.6 Illustrative examples 332

9.7 Summary 343.

Notes:

Title from publishers bibliographic system (viewed on 02 Mar 2012).

Other Format:

Print version:

ISBN:

9780511975837

9780521876285

Access Restriction:

Restricted for use by site license.