Leveraging Contextual Cues for Generating Basketball Highlights
Venue
ACM Multimedia (2016) (to appear)
Publication Year
2016
Authors
Vinay Bettadapura, Caroline Pantofaru, Irfan Essa
BibTeX
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
participants, the highlights produced by our system are comparable to the ones
produced by ESPN for the same games.
