Am I a Baller? Basketball Skill Assessment using First-Person Cameras

TitleAm I a Baller? Basketball Skill Assessment using First-Person Cameras
Publication TypeMiscellaneous
Year of Publication2016
AuthorsBertasius, G., Yu S. X., Park H. Soo, & Shi J.
Keywordsfirst-person videos, skill assessment

Skill assessment is a fundamental problem in sports like basketball. Nowadays, basketball skill assessment is handled by basketball experts who evaluate a player's skill from unscripted third-person basketball game videos. However, due to a large distance between a camera and the players, a third-person video captures a low-resolution view of the players, which makes it difficult to 1) identify specific players in the video and 2) to recognize what they are doing. To address these issues, we use first-person cameras, which 1) provide a high-resolution view of a player's actions, and 2) also eliminate the need to track each player. Despite this, learning a basketball skill assessment model from the first-person data is still challenging, because 1) a player's actions of interest occur rarely, and 2) the data labeling requires using basketball experts, which is costly. To counter these problems, we introduce a concept of basketball elements, 1) which addresses a limited player's activity data issue, and 2) eliminates the reliance on basketball experts. Basketball elements define simple basketball concepts, making labeling easy even for non-experts. Basketball elements are also prevalent in the first-person data, which allows us to learn, and use them for a player's basketball activity recognition and his basketball skill assessment. Thus, our contributions include (1) a new task of assessing a player's basketball skill from an unscripted first-person basketball game video, (2) a new 10.3 hour long first-person basketball video dataset capturing 48 players and (3) a data-driven model that assesses a player's basketball skill without relying on basketball expert labelers.

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