Leveraging Contextual Cues for Generating Basketball Highlights
This work addresses the need for efficient highlight generation in sports videos, offering a domain-specific solution that is incremental by combining existing audio-visual cues with new contextual data.
The paper tackles the problem of automatically generating basketball highlights by leveraging contextual cues from the game environment, such as excitement levels, to rank and select clips. The results show that highlights produced by their system are comparable in quality to those created by ESPN for the same games, as validated through user studies.
The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.