Fundamentals of Object Tracking.
Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.
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Full text (MCPHS users only) |
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| Other Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge :
Cambridge University Press,
2011
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| Series: | Cambridge books online.
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
MARC
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| 100 | 1 | |a Challa, Subhash. | |
| 245 | 1 | 0 | |a Fundamentals of Object Tracking. |
| 260 | |a Cambridge : |b Cambridge University Press, |c 2011. | ||
| 300 | |a 1 online resource (390 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a 4.2.1 The nearest neighbor filter equations. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover; FUNDAMENTALS OF OBJECT TRACKING; Title; Copyright; Contents; Preface; 1: Introduction to object tracking; 1.1 Overview of object tracking problems; 1.1.1 Air space monitoring; 1.1.2 Video surveillance; 1.1.3 Weather monitoring; 1.1.4 Cell biology; 1.2 Bayesian reasoning with application to object tracking; 1.2.1 Bayes' theorem; 1.2.2 Application to object tracking; 1.3 Recursive Bayesian solution for object tracking; 1.3.1 The generalized object dynamics equation; 1.3.2 The generalized sensor measurement equation; 1.3.3 Generalized object state prediction and conditional densities. | |
| 505 | 8 | |a 1.3.4 Generalized object state prediction and update1.3.5 Generalized object state filtering; 1.3.6 Generalized object state estimates; 1.4 Summary; 2: Filtering theory and non-maneuvering object tracking; 2.1 The optimal Bayesian filter; 2.1.1 Object dynamics and sensor measurement equations; 2.1.2 The optimal non-maneuvering object tracking filter recursion; 2.2 The Kalman filter; 2.2.1 Derivation of the Kalman filter; 2.2.2 The Kalman filter equations; 2.3 The extended Kalman filter; 2.3.1 Linear filter approximations; 2.3.2 The extended Kalman filter equations. | |
| 505 | 8 | |a 2.4 The unscented Kalman filter2.4.1 The unscented transformation; 2.4.2 The unscented Kalman filter algorithm; 2.5 The point mass filter; 2.5.1 Transition and prediction densities; 2.5.2 The likelihood function and normalization factor; 2.5.3 Conditional density; 2.5.4 The point mass filter equations; 2.6 The particle filter; 2.6.1 The particle filter for single-object tracking; 2.6.2 The OID-PF for single-object tracking; 2.6.3 Auxiliary bootstrap filter for single-object tracking; 2.6.4 Extended Kalman auxiliary particle filter for single-object tracking; 2.7 Performance bounds. | |
| 505 | 8 | |a 2.8 Illustrative exampleAngle tracking; 2.9 Summary; 3: Maneuvering object tracking; 3.1 Modeling for maneuvering object tracking; 3.1.1 Single model via state augmentation; 3.1.2 Multiple-model-based approaches; 3.2 The optimal Bayesian filter; 3.2.1 Process, measurement and noise models; 3.2.2 The conditional density and the conditional model probability; 3.2.3 Optimal estimation; 3.3 Generalized pseudo-Bayesian filters; 3.3.1 Generalized pseudo-Bayesian filter of order 1; 3.3.2 Generalized pseudo-Bayesian filter of order 2; 3.4 Interacting multiple model filter. | |
| 505 | 8 | |a 3.4.1 The IMM filter equations3.5 Particle filters for maneuvering object tracking; 3.5.1 Bootstrap filter for maneuvering object tracking; 3.5.2 Auxiliary bootstrap filter for maneuvering object tracking; 3.5.3 Extended Kalman auxiliary particle filter for maneuvering object tracking; 3.6 Performance bounds; 3.7 Illustrative example; 3.8 Summary; 4: Single-object tracking in clutter; 4.1 The optimal Bayesian filter; 4.1.1 Object dynamics, sensor measurement and noise models; 4.1.2 Conditional density; 4.1.3 Optimal estimation; 4.2 The nearest neighbor filter. | |
| 520 | |a Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Linear programming. | |
| 650 | 0 | |a Programming (Mathematics) | |
| 650 | 2 | |a Programming, Linear | |
| 700 | 1 | |a Morelande, Mark R. | |
| 700 | 1 | |a Musicki, Darko. | |
| 700 | 1 | |a Evans, Robin J. | |
| 776 | 0 | 8 | |i Print version: |a Challa, Subhash. |t Fundamentals of Object Tracking. |d Cambridge : Cambridge University Press, ©2011 |z 9780521876285 |
| 830 | 0 | |a Cambridge books online. | |
| 852 | |b E-Collections |h ProQuest | ||
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