Data Science Asked by Kavee on July 23, 2021
I am building a Tennis stroke classification system using CNN.
I assume each stroke contains 3 steps/classes (‘Ready’, ‘Impact’, ‘Finish’). I want to train a model which will predict whether the input video contains these steps/classes in it.
I have tried training 3 models for each step as binary classification.
Example of one step model classes:
1 - ready
0 - not-ready(other incorrect steps).
But this method failed since there are more features in ‘not-ready’ class. I got only 4% accuracy.
Can anyone help me to find a solution for this problem.
Given that you have only 3 classes and that they closely depend on each other, I think it's worth trying a multiclass setting as WBM said. The idea is to label each video using the full combination of actions, since the maximum number of combinations is 2^3 = 8:
Probably some combinations of actions are impossible, so the number of classes is likely less than 8. Why this is a reasonable approach:
However note that this kind of method may require more data, in particular it needs to have enough instances for each class.
Answered by Erwan on July 23, 2021
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