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Ramanayaka D.H.
2023-028
Commits
ecaa19b6
Commit
ecaa19b6
authored
May 25, 2023
by
Parindya H.S.T
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@@ -13,22 +13,22 @@ Detect whether any part of the front foot is behind the bowling crease to determ
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@@ -13,22 +13,22 @@ Detect whether any part of the front foot is behind the bowling crease to determ
**Specific objectives**
**Specific objectives**
To implement whether any part of the front foot is behind the bowling crease to determine the delivery is not a no ball
t
here are
To implement whether any part of the front foot is behind the bowling crease to determine the delivery is not a no ball
. T
here are
three main approaches. Below show what the objectives need when using all the approaches.
three main approaches. Below show what the objectives need when using all the approaches.
•
Collect data
•
*Collect data*
For the model training purpose, it is required to collect the data. In this individual component two of the following scenarios
For the model training purpose, it is required to collect the data. In this individual component two of the following scenarios
will be considered.
will be considered.
• Moment where any part of the front foot is behind the bowling crease.(legal ball)
• Moment where any part of the front foot is behind the bowling crease.(legal ball)
• Moment where any part of the front foot is not behind the bowling crease.(no ball)
• Moment where any part of the front foot is not behind the bowling crease.(no ball)
•
Preprocessing of the data set
•
*Preprocessing of the data set*
Collect the footages of the players during their practice session and extract the frame.
Collect the footages of the players during their practice session and extract the frame.
Refine the images and organize them into two different folders one as no ball and other as legal ball.
Refine the images and organize them into two different folders one as no ball and other as legal ball.
•
Model training and testing
•
*Model training and testing*
To detect whether a ball is a no ball or a legal ball using machine learning, do a comparison
To detect whether a ball is a no ball or a legal ball using machine learning, do a comparison
between Convolutional Neural Network (CNN) model and pretrained model MobileNet, ResNet.
between Convolutional Neural Network (CNN) model and pretrained model MobileNet, ResNet.
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