How we are building a distributed multi-camera real-time sports tracking system using GStreamer and Rust

Keyboard shortcuts

Action

Key

Play / Pause

K or space

Mute / Unmute

Toggle fullscreen mode

Select next subtitles

Select next audio track

Show slide in full page or toggle automatic source change

Seek 5s backward

left arrow

Seek 5s forward

right arrow

Seek 10s backward

shift + left arrow or J

Seek 10s forward

shift + right arrow or L

Seek 60s backward

control + left arrow

Seek 60s forward

control + right arrow

Decrease volume

shift + down arrow

Increase volume

shift + up arrow

Decrease playback rate

Increase playback rate

Seek to end

end

Seek to beginning

beginning

Loading Click here to add:

Information on this media

334 views

At Spiideo we offer automated sports video solutions to our customers for recording, analysis and broadcasting. We use a multi-camera setup to create a stitched panoramic video with an AI assisted cameraman. This talk will showcase how we moved from a segment based system with a glass-to-glass latency of almost two minutes to a frame-based system with a latency of around three seconds. In those three seconds we need to perform stitching of multiple 4K streams, detect objects in each camera stream and predict where to aim the virtual camera to follow the action on the pitch. And we do all of this across multiple instances in the cloud. This was Spiideo’s first real use of GStreamer and we will talk about what we struggled with, what helped us (a lot) and what we still do not really (really) understand.

Creation date: Sept. 26, 2023

Speakers: Robin Gustavsson | Daniel Pendse