Computer Vision for Sports: Analytics and Visualization 2025

Name: Computer Vision for Sports: Analytics and Visualization 2025
Rating: 4.8 (4 reviews)

Tennis Player & Ball Detection and Tracking System, YOLOv8, and DeepSORT. Field Keypoints detection and homography.

Created byNeuralearn Dot AI

Last updated 11/2025

English

English [Auto],

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.

Course content

7 sections • 21 lectures • 2h 45m total length

Welcome1:27
Explore how computer vision powers sports analytics with zero-shot object detection, tracking players and the ball, key point estimation, shot type insights, and 2D pitch projection via homography.
What you'll learn3:28
Train a YOLOv8 model with Ultralytics to detect tennis ball positions, then use zero-shot grounding to locate players, track with DeepSort, and project the pitch via homography from keypoints.

Code0:01
Understanding YOLOv8 Format6:12
Train a YOLOv8 model to detect the tennis ball with a Kaggle dataset and Ultralytics. Apply label format (class, x, y, w, h) to locate ball's center and bounding box.
Loading Dataset and Training YOLOv8 Model6:20
Install ultralytics and kaggle, download and unzip the dataset, configure the data YAML, and train a YOLOv8 X model for small-ball detection with performance visualizations.
Running Inference on a single Image9:58
Learn how to run inference on a single image using a YOLO model with Ultralytics, detect a ball, and interpret bounding box formats and confidence thresholds.
Running Inference on a full Video12:14
Process every video frame to detect the ball with frame-by-frame inference, returning a bounding box per frame and drawing a label, then save the annotated video.

Code0:01
Understanding Grounding DINO MOdel and Zero-Shot Object Detection9:02
Apply grounding dino for zero-shot object detection, moving from closed-set to open-set detection using prompts and reference sentences to locate, distinguish, and modify objects.
Prompting the DINO Model to Get Bounding Boxes11:29
Apply grounding dino zero-shot object detection to detect tennis players and tennis balls in images and videos, using prompts and visualizing bounding boxes with AutoProcessor and AutoModel.
Carrying Out Inference on a Full Video9:39
Define a full-video inference for tennis players by implementing a frame-by-frame player detections method, producing bounding boxes and scores, and visualizing predictions, then apply tracking to distinguish players from bystanders.

Code0:01
Understanding DeepSORT and Implementing Player Tracking on Full Video10:29
Learn how to implement real-time player tracking with DeepSORT in sports videos, converting detections into tracked identities across frames, handling occlusions, and filtering extra players by distance.
Filtering out Non Players27:56
Build a per-frame tracking system with a global and frame tracker to record players' positions and IDs, compute Euclidean distances to identify the top movers, and filter out non-players.

Code0:01
Drawing the plane20:27
Learn how to project a tennis match onto a reference plane using homography, four-point calibration, and an OpenCV workflow that draws players, ball, and court lines.
Understanding Homography and using it to Project the Pitch on a Plane7:00
Apply homography to map the pitch to a plane with OpenCV, obtain projection metrics, and project players and the ball onto the plane for visualization.

Code0:01
Preparing the Keypoints Detection Dataset and training the YOLO-Pose Model15:28
Build a tennis court key points dataset and train the Ultralytics YOLO pose model to detect 14 key points, enabling automatic projection of court objects across varying camera views.
Running Inference on Full Video6:23
Train a yolo nano model with ultralytics, setting dataset path, epochs, and image size via api or cli. Perform inference to load weights and visualize key points on tennis court.

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)

Computer Vision for Sports: Analytics and Visualization 2025

What you'll learn

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Course content

Introduction2 lectures • 5min

Ball Object Detection with Ultralytics YOLOv85 lectures • 35min

Player Detection with Grounding DINO on Hugging Face4 lectures • 30min

Player Tracking Throughout the Video with DeepSORT3 lectures • 38min

Field Projection with Homography3 lectures • 27min

Key Point Detection with Ultralytics YOLO Pose Estimation Model3 lectures • 22min

Conclusion1 lecture • 8min

Requirements

Description

Who this course is for: