Tennis Video Analysis using PyTorch and YOLO

This project harnesses the capabilities of YOLO, a leading-edge object detection framework, to identify players and tennis balls with high precision. It incorporates advanced tracking algorithms to maintain object continuity across frames. Additionally, we have developed a custom convolutional neural network (CNN) to detect key court features. The GitHub repository is linked below for reference.

In this video, you will explore:

1. Leveraging Ultralytics and YOLOv8 for sophisticated object detection in both images and videos.

2. Customizing and fine-tuning YOLO models on bespoke datasets for enhanced performance.

3. Designing and training a CNN with PyTorch to accurately extract keypoints.

4. Employing advanced object tracking techniques to ensure precise tracking across multiple frames.

5. Utilizing OpenCV (CV2) for comprehensive video processing, including reading, manipulating, and saving.

6. Analyzing detection data to adopt a data-driven methodology for feature development.