Tracking with Particle Filter for High-dimensional Observation and State Spaces:
This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments,...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Somerset
Wiley
2015
|
| Edition: | 1st ed |
| Subjects: | |
| Summary: | This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 online resource (223 pages) |
| ISBN: | 9781119053910 9781119004868 |
Staff View
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| 520 | |a This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Computer vision -- Mathematical models | |
| 650 | 4 | |a Particle methods (Numerical analysis) | |
| 650 | 4 | |a Pattern recognition systems | |
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| author | Dubuisson, Séverine |
| author_facet | Dubuisson, Séverine |
| author_role | aut |
| author_sort | Dubuisson, Séverine |
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| dewey-full | 006.37 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV043615862 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:30:55Z |
| institution | BVB |
| isbn | 9781119053910 9781119004868 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029029921 |
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| physical | 1 online resource (223 pages) |
| psigel | ZDB-30-PQE |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Wiley |
| record_format | marc |
| spelling | Dubuisson, Séverine Verfasser aut Tracking with Particle Filter for High-dimensional Observation and State Spaces 1st ed Somerset Wiley 2015 © 2014 1 online resource (223 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources This title concerns the use of a particle filter framework to track objects defined in high-dimensional state-spaces using high-dimensional observation spaces. Current tracking applications require us to consider complex models for objects (articulated objects, multiple objects, multiple fragments, etc.) as well as multiple kinds of information (multiple cameras, multiple modalities, etc.). This book presents some recent research that considers the main bottleneck of particle filtering frameworks (high dimensional state spaces) for tracking in such difficult conditions Mathematisches Modell Computer vision -- Mathematical models Particle methods (Numerical analysis) Pattern recognition systems Erscheint auch als Druck-Ausgabe Dubuisson, Séverine Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| spellingShingle | Dubuisson, Séverine Tracking with Particle Filter for High-dimensional Observation and State Spaces Mathematisches Modell Computer vision -- Mathematical models Particle methods (Numerical analysis) Pattern recognition systems |
| title | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_auth | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_exact_search | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_full | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_fullStr | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_full_unstemmed | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_short | Tracking with Particle Filter for High-dimensional Observation and State Spaces |
| title_sort | tracking with particle filter for high dimensional observation and state spaces |
| topic | Mathematisches Modell Computer vision -- Mathematical models Particle methods (Numerical analysis) Pattern recognition systems |
| topic_facet | Mathematisches Modell Computer vision -- Mathematical models Particle methods (Numerical analysis) Pattern recognition systems |
| work_keys_str_mv | AT dubuissonseverine trackingwithparticlefilterforhighdimensionalobservationandstatespaces |