How to Build a Football Analysis Project from Scratch

⚽Use YOLO, one of the best AI object detection models, to detect and track players, referees, and footballs in the video.

👕Segment and cluster pixels with K-means to assign players to teams based on the t-shirt colors they are wearing.

📊Calculate a team's ball acquisition percentage using the detected t-shirt pixels within player bounding boxes.

🌊Measure camera movement using optical flow to analyze player movement throughout the video.

📐Implement perspective transformation to accurately represent the scene's depth and measure player movement in meters.

What is YOLO?

—YOLO is an AI object detection model that can detect and locate objects in images or videos with high accuracy.

How does K-means clustering work?

—K-means clustering is a machine learning algorithm that groups data points into clusters based on similarity. It is commonly used in image segmentation tasks.

What is optical flow?

—Optical flow measures the perceived motion of objects between consecutive frames in a video. It is useful for analyzing movement and tracking objects.

What is a perspective transformation?

—A perspective transformation is a geometric transformation that maps the 3D world coordinates to a 2D image by considering the camera's viewpoint. It is used to correct distortions and accurately represent depth.

How can this project improve my machine learning skills?

—This project covers various machine learning concepts such as object detection, segmentation, motion analysis, and perspective transformation. By implementing these techniques, you can gain hands-on experience and enhance your understanding of computer vision.

00:00Learn how to build a football analysis project from scratch using YOLO for object detection, K-means for segmentation, and optical flow for movement analysis.

00:06Use YOLO to detect and track players, referees, and footballs in the video.

00:21Segment and cluster pixels with K-means to assign players to teams based on t-shirt colors.

00:35Calculate a team's ball acquisition percentage using the detected t-shirt pixels within player bounding boxes.

00:41Measure camera movement using optical flow to analyze player movement throughout the video.

00:50Implement perspective transformation to accurately represent the scene's depth and measure player movement in meters.