Description
Computer Vision for Sports: Analytics and Visualization is a course on computer vision and artificial intelligence techniques for analyzing sports performance, tracking player movements, and generating meaningful visual insights from video data, published by Udemy Online Academy. In this comprehensive and hands-on project, you will build a complete, comprehensive tennis analytics system from scratch. We won’t just learn the theory; we will implement a complete pipeline using a cutting-edge 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, coherent application that transforms raw video into actionable data.
Designed for data scientists, AI engineers, sports analysts, and tech enthusiasts, this course covers the complete workflow of extracting information from sports footage using modern computer vision models. Individuals will learn techniques for object recognition, player tracking, state estimation, event detection, performance analysis, and data visualization. By the end of this course, you will not only have a deep understanding of modern computer vision techniques, but also a stunning and valuable project that demonstrates your ability to build real-world AI solutions.
What you will learn in Computer Vision for Sports: Analytics and Visualization :
- Build a complete and comprehensive sports analytics system using Python.
- Train and implement a YOLOv8 model for high-speed, real-time ball detection
- Use Grounding DINO to detect players using text messages (zero shot detection).
- Implement DeepSORT to track multiple players and preserve their unique identities.
- Master homography with OpenCV to transform the camera view into a top-down 2D map.
- Train a custom YOLOv8-Pose model to accurately detect key points on the field.
- Visualize player and ball movements on a 2D tactical map for strategic analysis.
- Combine multiple AI models into a single, coherent data pipeline.
- Develop a valuable 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.
- Preparing custom datasets for training advanced computer vision models.
- And …
Course specifications
Publisher: Udemy
Instructors: Neuralearn Dot AI
Language: English
Level: Introductory to Advanced
Number of Lessons: 21
Duration: 2 hours and 45 minutes
Course topics
Computer Vision for Sports: Analytics and Visualization Prerequisites
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
Pictures
Computer Vision for Sports: Analytics and Visualization introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
1.9 GB
