Learning to track the visual motion of contours
A development of a method for tracking visual contours is described. Given an “untrained” tracker, a training motion of an object can be observed over some extended time and stored as an image sequence. The image sequence is used to learn parameters in a stochastic differential equation model. These...
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| Veröffentlicht in: | Artificial intelligence 1995-10, Vol.78 (1), p.179-212 |
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| container_title | Artificial intelligence |
| container_volume | 78 |
| creator | Blake, Andrew Isard, Michael Reynard, David |
| description | A development of a method for tracking visual contours is described. Given an “untrained” tracker, a training motion of an object can be observed over some extended time and stored as an image sequence. The image sequence is used to learn parameters in a stochastic differential equation model. These are used, in turn, to build a tracker whose predictor imitates the motion in the training set. Tests show that the resulting trackers can be markedly tuned to desired curve shapes and classes of motions. |
| doi_str_mv | 10.1016/0004-3702(95)00032-1 |
| format | Article |
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| fulltext | fulltext |
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| ispartof | Artificial intelligence, 1995-10, Vol.78 (1), p.179-212 |
| issn | 0004-3702 1872-7921 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_57361601 |
| source | Elsevier ScienceDirect Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Artificial intelligence Computers Information technology New technology Tracking Vision processing |
| title | Learning to track the visual motion of contours |
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