I am just getting into visual machine learning (currently a mobile developer) and have a challenging project of interest.
It involves using video as an input to then determine if a baseball player should have made a catch. I am imagining this should be viewed as a classification type problem using supervised learning. My available training set is +100,000 examples (video input, YES/NO output)
The new data (video) input I am hoping to use is either a panning or fixed-view (non-panning) video that captures a baseball being hit and an outfielder attempting to catch the ball
The desired classification: Should that have been a catch (not concerned with whether it was a catch), YES or NO
My question is:
I have seen algorithms successfully classify objects in video among a few other video machine learning perception tasks but I have been unsuccessful in identifying something similar to the project I am working on. I am hoping to be pointed in a good direction or it be explained the algorithms, etc relevant in attacking this problem.
Any help or thoughts are greatly appreciated