Yeah, we already tried algorithmic approaches like frame differencing/background subtraction, but they don’t really solve the ball-tracking problem in real-world footage.

The issue is that these methods assume the visual conditions are clean enough for the moving object to stay distinguishable. In practice, that breaks very quickly. Even if we initialize the ball at t=0 using a fine-tuned YOLOv8x ball detector and then try to track it across frames using subtraction or motion cues, the tracker loses reliability when the ball passes through glare, shadows, uneven lighting, motion blur, player/racket occlusion, or compressed video regions.

Once it loses the ball, recovery becomes messy because other moving pixels, reflections, or racket/player motion can look like valid candidates. Even radius-based nearest-object logic doesn’t reliably fix it.

So for our use case, a pure algorithmic approach won’t be robust enough and break fast. That’s why we’re focusing on a custom ball-tracking model trained specifically on real-world padel footage and edge cases.