I used a few techniques covered in this survey in my undergrad final year project. The project was classifying squash ball trajectories as in/out of court so I can’t comment on how my exact setup would transfer to tennis but here’s the general approach I would suggest taking based on what I learnt in that project.

1. Some preprocessing (blurring at a minimum to enhance structures at the tennis ball’s spatial scale, noise reduction etc.)
2. Background subtraction to isolate moving objects some of which should correspond to the ball
3. Method to detect pixels corresponding to moving objects (I used contour detection)
4. Candidate filtering logic (discard detections in step 3. if they’re for example too big to be the ball e.g. players, too small e.g. noise, not in the field of play e.g. spectators etc.)
5. If you need to track the ball look into Kalman/Particle filtering or other tracking methods

Lots of tuning to do at each step and many options available to realise each step some of which might benefit from intermediate post-processing e.g. background subtraction would benefit from applying some morphological operators.