Computer Vision for Sports Analytics and Visualization 2025

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Computer Vision for Sports Analytics and Visualization 2025 - Courses2025 - 11-18-2025

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Free Download Computer Vision for Sports Analytics and Visualization 2025
Published 11/2025
Created by Neuralearn Dot AI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 21 Lectures ( 2h 45m ) | Size: 1.83 GB

Tennis Player & Ball Detection and Tracking System, YOLOv8, and DeepSORT. Field Keypoints detection and homography.
What you'll learn
Build a complete, end-to-end sports analytics system using Python.
Train and implement a YOLOv8 model for high-speed, real-time ball detection
Use Grounding DINO to detect players with text prompts (zero-shot detection).
Implement DeepSORT to track multiple players and maintain their unique identities.
Master homography with OpenCV to transform a camera view into a 2D top-down map.
Train a custom YOLOv8-Pose model to accurately detect key points on a court.
Visualize player and ball movements on a 2D tactical map for strategic analysis.
Combine multiple AI models into a single, cohesive data pipeline.
Develop a portfolio-worthy project in the exciting field of AI in sports.
Understand the core principles of object detection, tracking, and perspective transformation.
Process and analyze complex video data to extract meaningful insights.
Prepare custom datasets for training advanced computer vision models.
Requirements
Basic Python Programming Skills
Fundamental Understanding of Machine Learning
Basic Deep Learning Concepts
Experience with Jupyter Notebooks or Google Colab
Familiarity with data science and computer vision libraries
Description
Ever wonder how professional sports teams get their edge? How analysts track player performance with pinpoint accuracy, visualizing every movement to uncover winning strategies? The answer is Computer Vision.From the Premier League to the NBA, AI-driven analytics has revolutionized the world of sports. The ability to automatically track players, detect the ball, and analyze game-flow from video footage is one of the most exciting and in-demand skills in the AI industry today.But while many tutorials show you how to detect an object in a single image, they stop there. The real magic happens when you track that object, understand its context on the field of play, and visualize its movement in a way that provides powerful insights. This is the gap between a simple script and a professional-grade sports analytics system.This course is designed to bridge that gap.In this comprehensive, hands-on project, you will build a complete, end-to-end tennis analytics system from scratch. We won't just learn theory; we will implement a full pipeline using a state-of-the-art technology stack, including Python, Ultralytics YOLOv8, DeepSORT, Grounding DINO, and OpenCV. You will learn how to combine multiple advanced AI models to create a single, cohesive application that turns raw video into actionable data.By the end of this course, you will not only have a deep understanding of modern computer vision techniques, but you will also have a stunning, portfolio-worthy project that demonstrates your ability to build real-world AI solutions.What you'll learn:Real-Time Ball Detection: Train and implement the state-of-the-art YOLOv8 model to accurately detect a tennis ball in video footage.Zero-Shot Player Detection: Use the powerful Grounding DINO model to detect players using text prompts, without needing to train on a labeled player dataset.Multi-Object Player Tracking: Implement DeepSORT to assign unique IDs to each player and track their movements consistently throughout the match, even during fast-paced rallies.Court Key Point Detection: Train a custom YOLOv8-Pose model to identify the 14 key points of a tennis court, forming the foundation for our projection.2D Court Projection with Homography: Master the concept of homography using OpenCV to transform the camera's perspective into a top-down, 2D tactical map.Dynamic Data Visualization: Project the real-time positions of players and the ball onto the 2D court map, creating a powerful visualization for strategic analysis.Building a Complete AI Pipeline: Learn how to seamlessly integrate all these components-detection, tracking, and projection-into a single, robust analytics system.If you are a developer, an aspiring AI engineer, or a data scientist passionate about sports and looking to build a truly impressive project, this course is for you.Enroll now and let's build the future of sports analytics together
Who this course is for
Software Devleopers
Aspiring AI & Machine Learning Engineers
Data Scientists
Sports Analytics Enthusiasts
Computer Science Students (Upper-Level Undergrad or Grad)
Homepage
https://www.udemy.com/course/computer-vision-for-sports-analytics-and-visualization-2025/

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