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Fundamentals of object tracking / / Subhash Challa [and others] [[electronic resource]]
| Fundamentals of object tracking / / Subhash Challa [and others] [[electronic resource]] |
| Autore | Challa Sudha <1953-> |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2011 |
| Descrizione fisica | 1 online resource (xii, 375 pages) : digital, PDF file(s) |
| Disciplina | 519.7 |
| Soggetto topico |
Linear programming
Programming (Mathematics) |
| ISBN |
1-280-88667-6
1-139-00985-0 9786613727985 1-139-00823-4 1-139-01037-9 1-139-00932-X 0-511-97583-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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
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 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 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 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 4.2.1 The nearest neighbor filter equations |
| Record Nr. | UNINA-9910457534403321 |
Challa Sudha <1953->
|
||
| Cambridge : , : Cambridge University Press, , 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fundamentals of object tracking / / Subhash Challa [and others] [[electronic resource]]
| Fundamentals of object tracking / / Subhash Challa [and others] [[electronic resource]] |
| Autore | Challa Sudha <1953-> |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2011 |
| Descrizione fisica | 1 online resource (xii, 375 pages) : digital, PDF file(s) |
| Disciplina | 519.7 |
| Altri autori (Persone) | ChallaSudha <1953-> |
| Soggetto topico |
Linear programming
Programming (Mathematics) |
| ISBN |
1-280-88667-6
1-139-00985-0 9786613727985 1-139-00823-4 1-139-01037-9 1-139-00932-X 0-511-97583-X |
| Classificazione | MAT017000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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
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 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 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 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 4.2.1 The nearest neighbor filter equations |
| Record Nr. | UNINA-9910781960203321 |
|
Challa Sudha <1953->
|
||
| Cambridge : , : Cambridge University Press, , 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||