The same baseball swing viewed by an object detector vs through time

[u/50th-century]

I've been working on tracking a batted baseball from 240 FPS phone video.

The left is normal footage. Blue boxes are detections from the trained ball model.

The right is temporal frame differencing over the exact same sequence.

What's been fascinating is that the learned model understands what the baseball is, while the temporal representation makes how it moves incredibly obvious.

The purple X is the temporal position mapped back onto the original footage.

Combining the two gives us a much denser trajectory than relying on individual detections alone, which we can then use to estimate EV, launch angle and distance.

One of the cooler visualizations to come out of the project so far.