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Tracking with particle filter for high-dimensional observation and state spaces / / Séverine Dubuisson
| Tracking with particle filter for high-dimensional observation and state spaces / / Séverine Dubuisson |
| Autore | Dubuisson Séverine |
| Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (223 p.) |
| Disciplina | 006.37 |
| Collana | Digital Signal and Image Processing Series |
| Soggetto topico |
Computer vision - Mathematical models
Pattern recognition systems Particle methods (Numerical analysis) |
| ISBN |
1-119-05405-2
1-119-00486-1 1-119-05391-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Notations; Introduction; 1: Visual Tracking by Particle Filtering; 1.1. Introduction; 1.2. Theoretical models; 1.2.1. Recursive Bayesian filtering; 1.2.2. Sequential Monte-Carlo methods; 1.2.2.1. Importance sampling; 1.2.2.2. Particle filter; 1.2.3. Application to visual tracking; 1.2.3.1. State model; 1.2.3.2. Observation model; 1.2.3.3. Importance function; 1.2.3.4. Likelihood function; 1.2.3.5. Resampling methods; 1.3. Limits and challenges; 1.4. Scientific position; 1.5. Managing large sizes in particle filtering; 1.6. Conclusion
2: Data Representation Models2.1. Introduction; 2.2. Computation of the likelihood function; 2.2.1. Exploitation of the spatial redundancy; 2.2.1.1. Optimal order for histogram computation; 2.2.1.2. Optimization of the integral histogram; 2.2.2. Exploitation of the temporal redundancy; 2.2.2.1. Temporal histogram; 2.2.2.2. Incremental distance between histograms; 2.3. Representation of complex information; 2.3.1. Representation of observations for movement detection, appearances and disappearances; 2.3.2. Representation of deformations; 2.3.3. Multifeature representation 2.3.3.1. Multimodal tracking2.3.3.2. Multifragment tracking; 2.3.3.3. Multiappearance tracking; 2.4. Conclusion; 3: Tracking Models That Focus on the State Space; 3.1. Introduction; 3.2. Data association methods for multi-object tracking; 3.2.1. Particle filter with adaptive classification; 3.2.2. Energetic filter for data association; 3.3. Introducing fuzzy information into the particle filter; 3.3.1. Fuzzy representation; 3.3.2. Fuzzy spatial relations; 3.3.3. Integration of fuzzy spatial relations into the particle filter; 3.3.3.1. Application to tracking an object with erratic movements 3.3.3.2. Application to multi-object tracking3.3.3.3. Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. Conclusion; 4: Models of Tracking by Decomposition of the State Space; 4.1. Introduction; 4.2. Ranked partitioned sampling; 4.3. Weighted partitioning with permutation of sub-particles; 4.3.1. Permutation of sub-samples; 4.3.2. Decrease the number of resamplings; 4.3.3. General algorithm and results; 4.4. Combinatorial resampling; 4.5. Conclusion; 5: Research Perspectives in Tracking and Managing Large Spaces 5.1. Tracking for behavioral analysis: toward finer tracking of the "future" and the "now"5.2. Tracking for event detection: toward a top-down model; 5.3. Tracking to measure social interactions; Bibliography; Index |
| Record Nr. | UNINA-9910132298103321 |
Dubuisson Séverine
|
||
| London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Tracking with particle filter for high-dimensional observation and state spaces / / Séverine Dubuisson
| Tracking with particle filter for high-dimensional observation and state spaces / / Séverine Dubuisson |
| Autore | Dubuisson Séverine |
| Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (223 p.) |
| Disciplina | 006.37 |
| Collana | Digital Signal and Image Processing Series |
| Soggetto topico |
Computer vision - Mathematical models
Pattern recognition systems Particle methods (Numerical analysis) |
| ISBN |
1-119-05405-2
1-119-00486-1 1-119-05391-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Notations; Introduction; 1: Visual Tracking by Particle Filtering; 1.1. Introduction; 1.2. Theoretical models; 1.2.1. Recursive Bayesian filtering; 1.2.2. Sequential Monte-Carlo methods; 1.2.2.1. Importance sampling; 1.2.2.2. Particle filter; 1.2.3. Application to visual tracking; 1.2.3.1. State model; 1.2.3.2. Observation model; 1.2.3.3. Importance function; 1.2.3.4. Likelihood function; 1.2.3.5. Resampling methods; 1.3. Limits and challenges; 1.4. Scientific position; 1.5. Managing large sizes in particle filtering; 1.6. Conclusion
2: Data Representation Models2.1. Introduction; 2.2. Computation of the likelihood function; 2.2.1. Exploitation of the spatial redundancy; 2.2.1.1. Optimal order for histogram computation; 2.2.1.2. Optimization of the integral histogram; 2.2.2. Exploitation of the temporal redundancy; 2.2.2.1. Temporal histogram; 2.2.2.2. Incremental distance between histograms; 2.3. Representation of complex information; 2.3.1. Representation of observations for movement detection, appearances and disappearances; 2.3.2. Representation of deformations; 2.3.3. Multifeature representation 2.3.3.1. Multimodal tracking2.3.3.2. Multifragment tracking; 2.3.3.3. Multiappearance tracking; 2.4. Conclusion; 3: Tracking Models That Focus on the State Space; 3.1. Introduction; 3.2. Data association methods for multi-object tracking; 3.2.1. Particle filter with adaptive classification; 3.2.2. Energetic filter for data association; 3.3. Introducing fuzzy information into the particle filter; 3.3.1. Fuzzy representation; 3.3.2. Fuzzy spatial relations; 3.3.3. Integration of fuzzy spatial relations into the particle filter; 3.3.3.1. Application to tracking an object with erratic movements 3.3.3.2. Application to multi-object tracking3.3.3.3. Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. Conclusion; 4: Models of Tracking by Decomposition of the State Space; 4.1. Introduction; 4.2. Ranked partitioned sampling; 4.3. Weighted partitioning with permutation of sub-particles; 4.3.1. Permutation of sub-samples; 4.3.2. Decrease the number of resamplings; 4.3.3. General algorithm and results; 4.4. Combinatorial resampling; 4.5. Conclusion; 5: Research Perspectives in Tracking and Managing Large Spaces 5.1. Tracking for behavioral analysis: toward finer tracking of the "future" and the "now"5.2. Tracking for event detection: toward a top-down model; 5.3. Tracking to measure social interactions; Bibliography; Index |
| Record Nr. | UNINA-9910828345603321 |
|
Dubuisson Séverine
|
||
| London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||