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) |
|
|
21 | (1) |
|
2 Filtering theory and non-maneuvering object tracking |
|
|
22 | (40) |
|
2.1 The optimal Bayesian filter |
|
|
22 | (3) |
|
|
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) |
|
|
46 | (7) |
|
|
53 | (4) |
|
|
57 | (3) |
|
|
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) |
|
|
97 | (2) |
|
|
99 | (3) |
|
|
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) |
|
|
126 | (5) |
|
4.7 Illustrative examples |
|
|
131 | (1) |
|
|
132 | (1) |
|
5 Single- and multiple-object tracking in clutter: object-existence-based approach |
|
|
133 | (90) |
|
|
133 | (5) |
|
5.2 Problem statement/models |
|
|
138 | (4) |
|
|
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) |
|
|
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) |
|
|
237 | (7) |
|
6.4 Object-existence-based tracking filters |
|
|
244 | (16) |
|
|
260 | (2) |
|
|
262 | (2) |
|
|
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) |
|
|
283 | (5) |
|
|
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) |
|
|
302 | (9) |
|
|
311 | (1) |
|
9 Practical object tracking |
|
|
312 | (32) |
|
|
312 | (1) |
|
9.2 Linear multi-target tracking |
|
|
313 | (4) |
|
9.3 Clutter measurement density estimation |
|
|
317 | (5) |
|
|
322 | (7) |
|
|
329 | (3) |
|
9.6 Illustrative examples |
|
|
332 | (11) |
|
|
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 | |