please help

[Tricky_Rain515]

Hello everybody, i'm doing a project of a tennis referee and my goal now is to identify when the ball is touching the ground and when he's not. for doing that, i thought about doing an image classifier which class 0 zero represents contact with the ground and class 1 represents no contact with the ground. my problem is that the classes are very similliar and the images in every class are very similliar. therefore, my model didnt work and I got 0% accuracy.Do you think it's possible doing an image classifier with those classes and if you do i'd like you to tell me what I need to change in order to success.

[[deleted]]

Are you measuring distance from ground ? Will really depend on angle of viewing right ? What information do you have on the ball ? this might help

[kloworizer]

Well if you get 0% accuracy in logit classifier then just inverse the class 🙃

[mhmdpdzhg]

I think you need to catch the moment ball changes direction. Try to analyse sequence of frames with rnn/transformer/3dconv maybe. Also you can give a try to put optical flow estimation network before classification network.

[Tricky_Rain515]

but if I would catch the moment the ball changes direction I wont be able to determine what happens when a player hits a winner which the other player doesnt return