Laboratories for automatic sport's media processing with AWS AI & ML services.
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Each of the following labs include a Jupyter notebook for following the instructions in the cells and code; the notebooks are designed to be deployed with Amazon SageMaker. You can either use SageMaker Studio IDE or a traditional SageMaker Notebook instance. For instructions on how to onboard with SageMaker Studio visit: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-studio-onboard.html
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For starting the work with the labs first clone this repository to your notebook environment. In SageMaker Studio go to "File", "New", "Terminal", then in the new terminal window write:
git clone https://github.com/rodzanto/ml-sports-media
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Go to the file browser in the left pane, you should now see the folder with the copy of the "ml-sports-media" repository. You can access the Lab #1 and open the corresponding Jupyter notebook. Follow the instructions in the notebook.
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Proceed with Lab #2.
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Proceed with Lab #3.
Goal: Classify and track objects (e.g. ball, specific players) in stored videos for sports (e.g. football, basketball). Rely on Amazon SageMaker for this (Ground Truth, Training, Endpoints).
Goal: Classify activities (e.g. shot, pass, nice play) in stored videos for sports (e.g. football, basketball). Rely on Amazon SageMaker for this (Ground Truth, Training, Endpoints).
Goal: Process videos of sports based on activity classification for automatically clipping highlights with relevant plays. Rely on AWS AI Services for this (Amazon Transcribe, Amazon Textract, Amazon Rekognition, etc.).