Leveraging Contextual Cues for Generating Basketball Highlights
Vinay Bettadapura, Caroline Pantofaru, Irfan Essa

TL;DR
This paper presents a novel approach for automatic basketball highlight generation that incorporates environmental contextual cues, resulting in highlights comparable to professional broadcaster edits.
Contribution
It introduces a new dataset with excitement annotations and explores the use of environmental cues, advancing automatic sports highlight generation beyond visual and audio features.
Findings
Highlights are comparable to ESPN's for study participants.
Environmental cues significantly improve highlight quality.
The approach outperforms previous methods using only visual and audio cues.
Abstract
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…
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Taxonomy
TopicsVideo Analysis and Summarization · Music and Audio Processing · Sports Analytics and Performance
