Abstract
This paper presents a method to assess a basketball player's performance from his/her first-person video. A key challenge lies in the fact that the evaluation metric is highly subjective and specific to a particular evaluator. We leverage the first-person camera to address this challenge. The spatiotemporal visual semantics provided by a first-person view allows us to reason about the camera wearer's actions while he/she is participating in an unscripted basketball game. Our method takes a player's first-person video and provides a player's performance measure that is specific to an evaluator's preference. To achieve this goal, we first use a convolutional LSTM network to detect atomic basketball events from first-person videos. Our network's ability to zoom-in to the salient regions addresses the issue of a severe camera wearer's head movement in first-person videos. The detected atomic events are then passed through the Gaussian mixtures to construct a highly non-linear visual spatiotemporal basketball assessment feature. Finally, we use this feature to learn a basketball assessment model from pairs of labeled first-person basketball videos, for which a basketball expert indicates, which of the two players is better. We demonstrate that despite not knowing the basketball evaluator's criterion, our model learns to accurately assess the players in real-world games. Furthermore, our model can also discover basketball events that contribute positively and negatively to a player's performance.
Original language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2196-2204 |
Number of pages | 9 |
ISBN (Electronic) | 9781538610329 |
DOIs | |
State | Published - Dec 22 2017 |
Event | 16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy Duration: Oct 22 2017 → Oct 29 2017 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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Volume | 2017-October |
ISSN (Print) | 1550-5499 |
Other
Other | 16th IEEE International Conference on Computer Vision, ICCV 2017 |
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Country/Territory | Italy |
City | Venice |
Period | 10/22/17 → 10/29/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.