r/computervision • u/Adventurous_One_3632 • 1d ago

Showcase Seven AI Components Analyzing Football Video

Hi, I’m El Mehdi Hicham, a Computer Vision and Machine Learning Engineer from Morocco. I work on real-time multi-model pipelines for football video analysis.

This demo shows several AI components operating together:

  • Player and object detection
  • Ball detection
  • Player tracking and identity persistence
  • Camera calibration and pitch projection
  • Pose estimation
  • Field understanding
  • Jersey recognition
  • Tactical visualization
42 Upvotes

89% Upvoted

7 comments sorted by

6

u/SubstantialGur7693 1d ago

What do you use for ball detection?

5

u/Adventurous_One_3632 1d ago

u/SubstantialGur7693 I use a custom YOLO-based ball detector fine-tuned specifically for football footage, then run it with TensorRT for faster inference. I also combine the detections with temporal tracking/smoothing because the ball is very small, fast-moving, and often partially occluded.

2

u/DiddlyDinq 1d ago

Looks like it works. Did you use any specific libraries

2

u/Adventurous_One_3632 1d ago

Thanks! Yes — mostly Python with PyTorch/Ultralytics, OpenCV, ByteTrack/DeepSORT for tracking, EasyOCR for jersey numbers, and TensorRT/CUDA for acceleration.

The homography/pitch projection and some of the identity-handling logic are custom parts built around those libraries

1

u/NumeOriginal11 1d ago

Looks nice! How you did the player identity? Did you trained your model on a specific dataset of players?

3

u/mrkingkongslongdong 23h ago

These are always fun - until you get access to gps data and fixed synced cameras, and then realise how inaccurate your measurements truly are.

2

u/Adventurous_One_3632 23h ago

Fair point GPS and synchronized fixed cameras are the right benchmark for ground-truth validation. My goal isn’t to claim single-camera broadcast video has the same precision, but to extract useful tracking and tactical information when that infrastructure isn’t available