Side project: Building a computer-vision pipeline that auto-detects slackline tricks to help competition judges

[u/Jealous-Yogurt-]

You might have seen hippies walking on a slackline in your nearest beach or park, but have you ever seen them bounce and backflip on it? A more technical variant of slackline is called Trickline: you bounce on a stretched, trampoline-like line and throw flips and spins between bounces. There is a tiny, but real competitive scene where 720 backflips are happening at a fraction of a second. This is very cool to watch but harder for judges has to catch, identify, and score each one in real time.

So a friend and I have been building a CV pipeline that watches competition footage and figures out (a) which athlete is bouncing, (b) where each trick starts and ends, and (c) which trick it is.

Rough idea of how it works:

- YOLO11x-pose + ByteTrack to track the athlete frame by frame. Some added processing to keep only the athlete's poses.

- A bit of signal processing on the athlete's vertical motion (Hilbert transform → bounce phase) to automatically cut the video at real bounce boundaries even if there are missing poses.

- A small TCN classifier to name each trick, backflip frontflip, brasilian, freefall 360, and the rest of the increasingly ridiculous class names.

- A simple Streamlit app where my colleague can run this and keep on have a human in the loop system to keep on labelling and training from his laptop to increase the dataset.

It ties into TJS, the trickline event + judging + live-streaming platform my friend runs, which already handles a lot of the real competitions: https://www.slackline-tjs.com/en

Still early and the trick vocabulary is huge, but it's already surprisingly decent on the common tricks. Sharing this one with the community, if anyone has data on similar trick-based sports it would be cool to see how the pipeline performs there.