  Esports has gained global popularity in recent years and several companies
have started offering live streaming videos of esports games and events. This
creates opportunities to develop large scale video understanding systems for
new product features and services. We present a technique for detecting
highlights from live streaming videos of esports game matches. Most video games
use pronounced visual effects to emphasize highlight moments; we use CNNs to
learn convolution filters of those visual effects for detecting highlights. We
propose a cascaded prediction approach that allows us to deal with several
challenges arise in a production environment. We demonstrate our technique on
our new dataset of three popular game titles, Heroes of the Storm, League of
Legends, and Dota 2. Our technique achieves 18 FPS on a single CPU with an
average precision of up to 83.18%. Part of our technique is currently deployed
in production on Yahoo Esports.

Computer Vision and Pattern Recognition