Model-based hand tracking using a hierarchical Bayesian filter
This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algori...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IEEE
2006
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| _version_ | 1826313463507451904 |
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| author | Stenger, B Thayananthan, A Torr, PHS Cipolla, R |
| author_facet | Stenger, B Thayananthan, A Torr, PHS Cipolla, R |
| author_sort | Stenger, B |
| collection | OXFORD |
| description | This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background. |
| first_indexed | 2024-09-25T04:13:47Z |
| format | Journal article |
| id | oxford-uuid:5973ff7f-2f8b-4cd5-ae1a-f80115e0e3b8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:13:47Z |
| publishDate | 2006 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:5973ff7f-2f8b-4cd5-ae1a-f80115e0e3b82024-07-12T13:33:35ZModel-based hand tracking using a hierarchical Bayesian filterJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5973ff7f-2f8b-4cd5-ae1a-f80115e0e3b8EnglishSymplectic ElementsIEEE2006Stenger, BThayananthan, ATorr, PHSCipolla, RThis paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background. |
| spellingShingle | Stenger, B Thayananthan, A Torr, PHS Cipolla, R Model-based hand tracking using a hierarchical Bayesian filter |
| title | Model-based hand tracking using a hierarchical Bayesian filter |
| title_full | Model-based hand tracking using a hierarchical Bayesian filter |
| title_fullStr | Model-based hand tracking using a hierarchical Bayesian filter |
| title_full_unstemmed | Model-based hand tracking using a hierarchical Bayesian filter |
| title_short | Model-based hand tracking using a hierarchical Bayesian filter |
| title_sort | model based hand tracking using a hierarchical bayesian filter |
| work_keys_str_mv | AT stengerb modelbasedhandtrackingusingahierarchicalbayesianfilter AT thayananthana modelbasedhandtrackingusingahierarchicalbayesianfilter AT torrphs modelbasedhandtrackingusingahierarchicalbayesianfilter AT cipollar modelbasedhandtrackingusingahierarchicalbayesianfilter |