Finding Athlete Speed Using YOLO

Discover how computer vision is revolutionizing sports analytics! 🚀 In this tutorial, we’ll show you how to estimate the real-time speed of athletes using YOLO and OpenCV. No wearables, no expensive sensors—just a camera and AI. Learn step-by-step from dataset creation, annotation, model training, to speed estimation with perspective transformation. Perfect for AI enthusiasts, sports analysts, and computer vision developers who want to bring data-driven insights into sports performance. What you’ll learn: - How to use YOLO for athlete detection - Dataset creation & annotation workflow - Speed estimation using perspective transformation - Real-time player tracking with computer vision Resources: - Notebook: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/blob/main/fine-tune%20YOLO%20for%20various%20use%20cases/Using-YOLO-For-Finding-Athlete-Speed.ipynb - Labellerr’s GitHub: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision Interested in learning more about our services? Website: https://www.labellerr.com Book a Demo: https://www.labellerr.com/book-a-demo Find us on Social Media Platforms: LinkedIn: https://www.linkedin.com/company/labellerr Twitter: https://x.com/Labellerr1 #computervision #sportsanalytics #YOLO #opencv #athletespeed #machinelearning #deeplearning #sportsAI #tennisanalytics #AI #tracking #speedestimation #sportstech #aivideoanalysis #datascience