Convert Any Sports Match into a 2D Bird's Eye View

Описание: 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

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