Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis
This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observi...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
IEEE Computer Society Conference Publishing Services
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/26678 |
| _version_ | 1848752054782132224 |
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| author | Peursum, Patrick Venkatesh, Svetha West, Geoffrey |
| author_facet | Peursum, Patrick Venkatesh, Svetha West, Geoffrey |
| author_sort | Peursum, Patrick |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model. The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate. |
| first_indexed | 2025-11-14T08:02:31Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26678 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:02:31Z |
| publishDate | 2007 |
| publisher | IEEE Computer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-266782017-01-30T12:54:43Z Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis Peursum, Patrick Venkatesh, Svetha West, Geoffrey This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model. The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate. 2007 Conference Paper http://hdl.handle.net/20.500.11937/26678 IEEE Computer Society Conference Publishing Services fulltext |
| spellingShingle | Peursum, Patrick Venkatesh, Svetha West, Geoffrey Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title | Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title_full | Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title_fullStr | Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title_full_unstemmed | Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title_short | Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis |
| title_sort | tracking-as-recognition for articulated full-body human motion analysis |
| url | http://hdl.handle.net/20.500.11937/26678 |