| Autor: |
Jaques, Miguel, Asenov, Martin, Burke, Michael, Hospedales, Timothy M |
| Přispěvatelé: |
Firoozi, Roya, Mehr, Negar, Yel, Esen, Antonova, Rika, Bohg, Jeannette, Schwager, Mac, Kochendorfer, Mykel |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Jaques, M, Asenov, M, Burke, M & Hospedales, T M 2022, Vision-based system identification and 3D keypoint discovery using dynamics constraints . in R Firoozi, N Mehr, E Yel, R Antonova, J Bohg, M Schwager & M Kochendorfer (eds), Proceedings of The 4th Annual Learning for Dynamics and Control Conference : Volume 168: Learning for Dynamics and Control Conference, 23-24 June 2022, Stanford University, Stanford, CA, USA . vol. 168, pp. 316-329, 4th Annual Learning for Dynamics & Control Conference, Stanford, California, United States, 23/06/22 . < https://proceedings.mlr.press/v168/jaques22a.html > |
| Popis: |
This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken from a static camera, using only the family of equations of motion of the object of interest as weak supervision. V-SysId takes keypoint trajectory proposals and alternates between maximum likelihood parameter estimation and extrinsic camera calibration, before applying a suitable selection criterion to identify the track of interest. This is then used to train a keypoint tracking model using supervised learning. Results on a range of settings (robotics, physics, physiology) highlight the utility of this approach. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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