I'm building a feature to automatically edit out all the downtime of a tennis video. I have a partial implementation that stores the start and end times of Vision trajectory detections and writes only those segments to an AVFoundation export session.
I've encountered a major issue, which is that the trajectories returned end whenever the ball bounce, so each segment is just one tennis shot and nowhere close to an entire rally with multiple bounces. I'm ensure if I should continue done the trajectory route, maybe stitching together the trajectories and somehow only splitting at the start and end of a rally.
Any general guidance would be appreciated.
Is there a different Vision or ML approach that would more accurately model the start and end time of a rally? I considered creating a custom action classifier to classify frames to be either "playing tennis" or "inactivity," but I started with Apple's trajectory detection since it was already built and trained. Maybe a custom classifier would be needed, but not sure.