Assessing the Quality of Actions
| dc.contributor.author | Pirsiavash, Hamed | |
| dc.contributor.author | Vondrick, Carl | |
| dc.contributor.author | Torralba, Antonio | |
| dc.date.accessioned | 2019-07-01T14:23:34Z | |
| dc.date.available | 2019-07-01T14:23:34Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | While recent advances in computer vision have provided reliable methods to recognize actions in both images and videos, the problem of assessing how well people perform actions has been largely unexplored in computer vision. Since methods for assessing action quality have many real-world applications in healthcare, sports, and video retrieval, we believe the computer vision community should begin to tackle this challenging problem. To spur progress, we introduce a learning-based framework that takes steps towards assessing how well people perform actions in videos. Our approach works by training a regression model from spatiotemporal pose features to scores obtained from expert judges. Moreover, our approach can provide interpretable feedback on how people can improve their action. We evaluate our method on a new Olympic sports dataset, and our experiments suggest our framework is able to rank the athletes more accurately than a non-expert human. While promising, our method is still a long way to rivaling the performance of expert judges, indicating that there is significant opportunity in computer vision research to improve on this difficult yet important task. | en_US |
| dc.description.sponsorship | Funding was provided by a NSF GRFP to CV and a Google research award and ONR MURI N000141010933 to AT. | en_US |
| dc.description.uri | https://link.springer.com/chapter/10.1007/978-3-319-10599-4_36 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | book chapters | en_US |
| dc.identifier | doi:10.13016/m2yzxo-ak1x | |
| dc.identifier.citation | Pirsiavash H., Vondrick C., Torralba A. (2014) Assessing the Quality of Actions. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8694. Springer, Cham | en_US |
| dc.identifier.uri | https://doi.org/10.1007/978-3-319-10599-4_36 | |
| dc.identifier.uri | http://hdl.handle.net/11603/14322 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer, Cham | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | Discrete Cosine Transform | en_US |
| dc.subject | Discrete Fourier Transform | en_US |
| dc.subject | Support Vector Regression | en_US |
| dc.subject | Action Recognition | en_US |
| dc.subject | Action Quality | en_US |
| dc.title | Assessing the Quality of Actions | en_US |
| dc.type | Text | en_US |