tennis-count

Counting scores of tennis balls

  1. Counting tennis balls from input: videos of tennis matches.
  2. Output: Each player score
  3. Steps and literature
     https://gml16.github.io/projects/Report_LV8_Project.pdf
  4. Action Recognition: https://medium.com/bakken-b%C3%A6ck/improving-your-tennis-game-with-computer-vision-863969743024
  5. Get tennis court from videos
  6. Track ball from tennis videos
  7. Track hit action --> Track ball trajectory --> Track bounce (position of bounce)
        --> get the position of the ball inside the court.
  8. Track Tennis Ball: https://nol.cs.nctu.edu.tw:234/open-source/TrackNet/
  9. Official TrackNet V2: https://nol.cs.nctu.edu.tw:234/open-source/TrackNetv2
  10. TrackNet: https://nol.cs.nctu.edu.tw/ndo3je6av9/
  11. https://medium.com/bakken-b%C3%A6ck/improving-your-tennis-game-with-computer-vision-863969743024 for Tracking OpenPose
  12. Positions of players around the net: https://github.com/sethah/deeptennis
  13. The stuffs here: https://towardsdatascience.com/ball-detection-with-computer-vision-ai-in-sports-f9ef743e0ef1
  14. This one is really awesome: https://github.com/vishaltiwari/bmvc-tennis-analytics
  15. https://github.com/vishaltiwari/bmvc-tennis-analytics

How did I setup the problem?

 * https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d
 * Game:

Milestones

 * Track Balls on Tennis Videos --> (x,y, frame) pos of moving ball in each frame.
 * Improved detection using Focal Lens and ResNet extension of TrackNet.
 * Next: build a classical filter to optimise the trajectory prediction.
 * How can I reduce the time complexity of detection?
   * Instead of running inference on each frame: propagate the dec
   ** TODO: Handle the case where we dont see the players:
   ** DONE: Run the detector for court detection then eliminate the wrong frames.
   ** TODO: Steps to perform bounce. (Test this here: )
   ** DONE: Tennis Court detection: https://github.com/gchlebus/tennis-court-detection
   ** Test algorithm to determine bounce from 2D Coordinates.
   **

Report:

 * https://leimao.github.io/blog/Focal-Loss-Explained/
 * Think what to write in the report --> Mention advantage Focal Loss,  ResNet and RetinaNet.
 * Mention why Tracking steps more      

Business Proposal of this project

 * $60,000 or more to set up on each court [1], reconstitutes shots in 3D

<<<<<<< HEAD * Filtering Big branch: https://thomas-cokelaer.info/blog/2018/02/git-how-to-remove-a-big-file-wrongly-committed/

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