Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This boo...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2002
|
| Ausgabe: | 1st ed. 2002 |
| Schriftenreihe: | Distinguished Dissertations
|
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory |
| Beschreibung: | 1 Online-Ressource (IX, 174 p) |
| ISBN: | 9781447106791 |
| DOI: | 10.1007/978-1-4471-0679-1 |
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| edition | 1st ed. 2002 |
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| id | DE-604.BV047064142 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:12:22Z |
| indexdate | 2024-07-10T09:01:34Z |
| institution | BVB |
| isbn | 9781447106791 |
| language | English |
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| publisher | Springer London |
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| series2 | Distinguished Dissertations |
| spelling | MacCormick, John Verfasser aut Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking by John MacCormick 1st ed. 2002 London Springer London 2002 1 Online-Ressource (IX, 174 p) txt rdacontent c rdamedia cr rdacarrier Distinguished Dissertations A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory Computer Imaging, Vision, Pattern Recognition and Graphics Algorithm Analysis and Problem Complexity Optical data processing Algorithms Erscheint auch als Druck-Ausgabe 9781447111764 Erscheint auch als Druck-Ausgabe 9781852336011 Erscheint auch als Druck-Ausgabe 9781447106807 https://doi.org/10.1007/978-1-4471-0679-1 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | MacCormick, John Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking Computer Imaging, Vision, Pattern Recognition and Graphics Algorithm Analysis and Problem Complexity Optical data processing Algorithms |
| title | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking |
| title_auth | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking |
| title_exact_search | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking |
| title_exact_search_txtP | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking |
| title_full | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking by John MacCormick |
| title_fullStr | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking by John MacCormick |
| title_full_unstemmed | Stochastic Algorithms for Visual Tracking Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking by John MacCormick |
| title_short | Stochastic Algorithms for Visual Tracking |
| title_sort | stochastic algorithms for visual tracking probabilistic modelling and stochastic algorithms for visual localisation and tracking |
| title_sub | Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking |
| topic | Computer Imaging, Vision, Pattern Recognition and Graphics Algorithm Analysis and Problem Complexity Optical data processing Algorithms |
| topic_facet | Computer Imaging, Vision, Pattern Recognition and Graphics Algorithm Analysis and Problem Complexity Optical data processing Algorithms |
| url | https://doi.org/10.1007/978-1-4471-0679-1 |
| work_keys_str_mv | AT maccormickjohn stochasticalgorithmsforvisualtrackingprobabilisticmodellingandstochasticalgorithmsforvisuallocalisationandtracking |