Sports Highlight Generator

Inspiration

Publishing highlights after a sports game is a mandatory practice followed in the broadcast industry. A highlights video provides a quick snapshot of the event highlighting the interesting events in the game and thus providing the viewers an opportunity to get a quick summary of the game. In a manual process, a video editor has to run through the entire game, identify candidate areas for the highlights and compile all the clips into a single compilation video. This can be a cumbersome process and time-consuming.

What it does

A Deep Learning Model that automatically generate highlights from a sports video using audience reaction. The highlights in the sports video are generated by analysing the key exciting moments in the match which attract the attention of the spectators in the match.

Project Demo: https://youtu.be/YmUVbaCkLxI

Original Video Link: https://www.youtube.com/watch?v=CGFgHjeEkbY

Highlight generated video: https://youtu.be/HagUinMhpbg

How we built it

Using numpy and moviepy in Python

Challenges we ran into

The algorithm can be confused by broadcasters who make lots of replays or lower the sound of the crowd after goals, and it may miscut some goals on penalties because the crowd starts whistling long before the shoot. So large-scale applications would require a less naive model.