Welcome to the world of sports technology! Today, we will learn how to use a powerful machine learning project to automatically overlay pitch motion and trajectory on your baseball pitching videos. Using cutting-edge techniques, this project simplifies your workload and enhances your analysis of throwing techniques. Let’s get started!

Project Overview

This project takes your baseball pitching clips, which can be sourced directly from your phone or camera, and automatically generates visual overlays. With the integration of a fine-tuned YOLOv4 model to detect the ball’s location and SORT tracking for motion tracking, we create a seamless viewing experience. Additionally, image registration techniques help mitigate minor camera shifts.

Getting Started

Here’s a step-by-step guide to setting up this project and generating your own pitching overlay clips:

1. Get a Copy of the Project

• First, you need to clone the project repository. Open your terminal and run:

git clone https://github.com/chonyy/ML-auto-baseball-pitching-overlay.git

2. Prerequisites

Before you can run the project, you must install the necessary dependencies. To do so, execute the following command in your terminal:

pip install -r requirements.txt

3. Overlay Your Clips!

Finally, run the project with your baseball clips:

• To try a sample video, run:

python pitching_overlay.py

• For your custom videos, place them in a folder, and specify the path in the command line:

python pitching_overlay.py --videos_folder ./videos

Understanding the Code: An Analogy

Imagine you are a coach, and you want to analyze how each player throws the ball. You use a high-tech magic camera that not only records their pitch motion but also automatically tracks and visualizes the ball’s path through the air!

In this setup:

• The YOLOv4 model is like your trained assistant who can spot the ball instantly.
• The SORT algorithm represents your ability to follow the player’s throws closely, ensuring each pitch is tracked individually.
• Lastly, image registration works like your video editing skills, aligning footage from different angles seamlessly.

Troubleshooting

If you run into any issues while setting up or executing the project, here are some troubleshooting tips:

• Ensure you have all the dependencies installed before running the project.
• If your videos are not processing, double-check that the video folder path is correct and that the videos are in a compatible format.
• If the overlay doesn’t display correctly, check for discrepancies in frame rates or resolutions between your original video and the processing settings.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With a few simple commands, you can generate insightful overlays for your baseball pitches, all thanks to the powerful integration of machine learning techniques. This innovation isn’t just fascinating; it’s a step toward leveraging technology in sports analytics!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.