Convert Any Sports Match into a 2D Projection Mapping скачать в хорошем качестве

Convert Any Sports Match into a 2D Projection Mapping

In this video, I demonstrate HoloField, an AI-powered system designed to solve the problem of perspective distortion in sports broadcasts. Standard camera angles make it difficult to judge a player's true positioning and court coverage, but by using Computer Vision, we can "flatten" the game into a precise, top-down tactical map. Key Technical Highlights: YOLO11 Integration: Identifying and tracking players with high-resolution segmentation masks. Perspective Transformation: Using mathematical mapping (Homography) to project feet positions from a 3D tilted view onto a flat 2D miniature court. Movement Smoothing: Cleaning up shaky data to ensure player markers glide realistically across the map. Tactical Insights: Visualizing court space and player movement in a shared metric environment. Whether you're into sports analytics, coaching, or AI development, this project shows how raw footage can be transformed into a professional tactical tool. Cookbook: https://github.com/Labellerr/Hands-On... Github: https://github.com/Labellerr chapters 0:00 Introduction: The Problem with Distorted Sports Broadcasts 0:43 Solution Overview: AI-Powered 2D Top-Down Court Mapping 0:50 Project Goals: Player Tracking & Tactical Analysis 1:15 Key Technical Features: YOLO 11X, Homography, Real-World Projection 1:50 Step 1: Importing Libraries & Cloning Helper Repository 2:18 Step 2: Extracting 50 Frames from Tennis Match Video 2:49 Step 3: Annotating Players & Ball on Labeler Platform 3:38 Step 4: Exporting Annotations & Converting COCO to YOLO Format 4:38 Step 5: Training YOLO 11X Segmentation Model 4:53 Step 6: Running Inference on Raw Video 5:14 Results: Player & Ball Detection Visualization 5:52 Step 7: Adding Trajectory Lines for Player Movement 6:22 Results: Smooth Trajectory Tracking 6:54 Step 8: Marking Calibration Points for Homography 7:16 Marking 7 Points: 4 Corners & 3 Center Line Points 8:15 Step 9: Building the Homography Matrix 8:46 Mapping Pixel Coordinates to Real-World Court Dimensions (11m x 24m) 9:01 Adding 2.5m Padding for Out-of-Bounds Movement 9:24 Step 10: Running Full Inference with Homography Projection 9:28 Results: Real-Time 2D Top-Down Map with Player Positions 10:06 Conclusion & Additional Resources 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:   / labellerr   Twitter: https://x.com/Labellerr1 #HoloField #SportsAI #ComputerVision #YOLO11 #TennisAnalytics #PythonProgramming #MachineLearning #SportsTech #TacticalAnalysis #OpenCV #ObjectTracking #DataScience #AIPoject #SportsCoaching #AIInSports