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E-raamat: Fundamentals of Object Tracking

, (University of Melbourne), (University of Melbourne), (University of Melbourne)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Jul-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139006064
  • Formaat - PDF+DRM
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  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Jul-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139006064

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"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"--

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Muu info

Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.
Preface ix
1 Introduction to object tracking
1(21)
1.1 Overview of object tracking problems
2(5)
1.2 Bayesian reasoning with application to object tracking
7(9)
1.3 Recursive Bayesian solution for object tracking
16(5)
1.4 Summary
21(1)
2 Filtering theory and non-maneuvering object tracking
22(40)
2.1 The optimal Bayesian filter
22(3)
2.2 The Kalman filter
25(6)
2.3 The extended Kalman filter
31(5)
2.4 The unscented Kalman filter
36(7)
2.5 The point mass filter
43(3)
2.6 The particle filter
46(7)
2.7 Performance bounds
53(4)
2.8 Illustrative example
57(3)
2.9 Summary
60(2)
3 Maneuvering object tracking
62(41)
3.1 Modeling for maneuvering object tracking
62(4)
3.2 The optimal Bayesian filter
66(6)
3.3 Generalized pseudo-Bayesian filters
72(12)
3.4 Interacting multiple model filter
84(7)
3.5 Particle filters for maneuvering object tracking
91(6)
3.6 Performance bounds
97(2)
3.7 Illustrative example
99(3)
3.8 Summary
102(1)
4 Single-object tracking in clutter
103(30)
4.1 The optimal Bayesian filter
104(3)
4.2 The nearest neighbor filter
107(4)
4.3 The probabilistic data association filter
111(8)
4.4 Maneuvering object tracking in clutter
119(3)
4.5 Particle filter for tracking in clutter
122(4)
4.6 Performance bounds
126(5)
4.7 Illustrative examples
131(1)
4.8 Summary
132(1)
5 Single- and multiple-object tracking in clutter: object-existence-based approach
133(90)
5.1 Introduction
133(5)
5.2 Problem statement/models
138(4)
5.3 Track state
142(5)
5.4 Optimal Bayes' recursion
147(24)
5.5 Optimal track update cycle
171(13)
5.6 Track component control
184(7)
5.7 Object-existence-based single-object tracking
191(14)
5.8 Object-existence-based multi-object tracking
205(16)
5.9 Summary
221(2)
6 Multiple-object tracking in clutter: random-set-based approach
223(42)
6.1 The optimal Bayesian multi-object tracking filter
225(2)
6.2 The probabilistic hypothesis density approximations
227(10)
6.3 Approximate filters
237(7)
6.4 Object-existence-based tracking filters
244(16)
6.5 Performance bounds
260(2)
6.6 Illustrative example
262(2)
6.7 Summary
264(1)
7 Bayesian smoothing algorithms for object tracking
265(24)
7.1 Introduction to smoothing
265(1)
7.2 Optimal Bayesian smoothing
266(2)
7.3 Augmented state Kalman smoothing
268(3)
7.4 Smoothing for maneuvering object tracking
271(4)
7.5 Smoothing for object tracking in clutter
275(3)
7.6 Smoothing with object existence uncertainty
278(5)
7.7 Illustrative example
283(5)
7.8 Summary
288(1)
8 Object tracking with time-delayed, out-of-sequence measurements
289(23)
8.1 Optimal Bayesian solution to the OOSM problem
289(4)
8.2 Single- and multi-lag OOSM algorithms
293(1)
8.3 Augmented state Kalman filter for multiple-lag OOSM
294(3)
8.4 Augmented state PDA filter for multiple-lag OOSM in clutter
297(5)
8.5 Simulation results
302(9)
8.6 Summary
311(1)
9 Practical object tracking
312(32)
9.1 Introduction
312(1)
9.2 Linear multi-target tracking
313(4)
9.3 Clutter measurement density estimation
317(5)
9.4 Track initialization
322(7)
9.5 Track merging
329(3)
9.6 Illustrative examples
332(11)
9.7 Summary
343(1)
Appendix A Mathematical and statistical preliminaries 344(10)
Appendix B Finite set statistics (FISST) 354(4)
Appendix C Pseudo-functions in object tracking 358(3)
References 361(9)
Index 370
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 Muicki 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.