The active recovery of 3D motion trajectories and their use in prediction
This paper describes the theory and real-time implementation using an active camera platform of a method of planar trajectory recovery, and of the use of those trajectories to facilitate prediction over delays in the visual feedback loop. Image-based position and velocity demands for tracking are ge...
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| Format: | Journal article |
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1997
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| _version_ | 1797063053447004160 |
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| author | Bradshaw, K Reid, I Murray, D |
| author_facet | Bradshaw, K Reid, I Murray, D |
| author_sort | Bradshaw, K |
| collection | OXFORD |
| description | This paper describes the theory and real-time implementation using an active camera platform of a method of planar trajectory recovery, and of the use of those trajectories to facilitate prediction over delays in the visual feedback loop. Image-based position and velocity demands for tracking are generated by detecting and segmenting optical flow within a central region of the image, and a projective construct is used to map the camera platform's joint angles into a Euclidean coordinate system within a plane, typically the ground plane, in the scene. A set of extended Kalman filters with different dynamics is implemented to analyze the trajectories, and these compete to provide the best description of the motion within an interacting multiple model. Prediction from the optimum motion model is used within the visual feedback loop to overcome visual latency. It is demonstrated that prediction from the 3D planar description gives better tracking performance than prediction based on a filtered description of observer-based 2D motion trajectories. ©1997 IEEE. |
| first_indexed | 2024-03-06T20:54:21Z |
| format | Journal article |
| id | oxford-uuid:38ac28a6-ada8-4674-ab68-221754ddaed2 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:54:21Z |
| publishDate | 1997 |
| record_format | dspace |
| spelling | oxford-uuid:38ac28a6-ada8-4674-ab68-221754ddaed22022-03-26T13:51:27ZThe active recovery of 3D motion trajectories and their use in predictionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:38ac28a6-ada8-4674-ab68-221754ddaed2Symplectic Elements at Oxford1997Bradshaw, KReid, IMurray, DThis paper describes the theory and real-time implementation using an active camera platform of a method of planar trajectory recovery, and of the use of those trajectories to facilitate prediction over delays in the visual feedback loop. Image-based position and velocity demands for tracking are generated by detecting and segmenting optical flow within a central region of the image, and a projective construct is used to map the camera platform's joint angles into a Euclidean coordinate system within a plane, typically the ground plane, in the scene. A set of extended Kalman filters with different dynamics is implemented to analyze the trajectories, and these compete to provide the best description of the motion within an interacting multiple model. Prediction from the optimum motion model is used within the visual feedback loop to overcome visual latency. It is demonstrated that prediction from the 3D planar description gives better tracking performance than prediction based on a filtered description of observer-based 2D motion trajectories. ©1997 IEEE. |
| spellingShingle | Bradshaw, K Reid, I Murray, D The active recovery of 3D motion trajectories and their use in prediction |
| title | The active recovery of 3D motion trajectories and their use in prediction |
| title_full | The active recovery of 3D motion trajectories and their use in prediction |
| title_fullStr | The active recovery of 3D motion trajectories and their use in prediction |
| title_full_unstemmed | The active recovery of 3D motion trajectories and their use in prediction |
| title_short | The active recovery of 3D motion trajectories and their use in prediction |
| title_sort | active recovery of 3d motion trajectories and their use in prediction |
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