Fundamentals of Object Tracking

by Subhash Challa, Mark R. Morelande, Darko Musicki, Robin J. Evans

Overview

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 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, multiobject, 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 successful implementation of tracking algorithms, such as track initialization and merging.

Product Details

ISBN-13:	9780521876285
Publisher:	Cambridge University Press
Publication date:	07/28/2011
Pages:	392
Product dimensions:	7.13(w) x 10.04(h) x 1.10(d)

About the Author

Subhash Challa is a Senior Principal Research Scientist at NICTA (National ICT Australia) VRL at the University of Melbourne (UoM). He is also one of the co-founders of SenSen Networks Pty Ltd and has been the Director and CTO of the company.

Mark R. Morelande is a Senior Research Fellow in the Melbourne Systems Laboratory at the University of Melbourne.

Darko Mušicki is a Professor in the Department of Electronic Systems Engineering at Hanyang University in Ansan, Republic of Korea.

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

Table of Contents

Preface; 1. Introduction to object tracking; 2. Filtering theory and non-maneuvering object tracking; 3. Maneuvering object tracking; 4. Single-object tracking in clutter; 5. Single- and multiple-object tracking in clutter: object-existence-based approach; 6. Multiple-object tracking in clutter: random-set-based approach; 7. Bayesian smoothing algorithms for object tracking; 8. Object tracking with time-delayed, out-of-sequence measurements; 9. Practical object tracking; A. Mathematical and statistical preliminaries; B. Finite set statistics (FISST); C. Pseudo-functions in object tracking; References; Index.