OwlTing/AI_basketball_games_video_editor
📦 Open Source ProjectOwlTing
Automated basketball highlight generation using PyTorch YOLOv4 computer vision for intelligent video editing.
The AI Basketball Games Video Editor is a specialized computer vision pipeline designed to parse long-form basketball game footage and isolate high-impact segments. At its core, the project utilizes the YOLOv4 object detection architecture implemented in PyTorch to track players, the ball, and specific game events. By training the model to recognize basketball-specific features, the system can automatically flag timestamps where significant gameplay occurs, such as shots or defensive plays. The repository provides the necessary scripts to process video streams, perform inference, and generate edited highlight clips. This tool serves as a practical implementation of real-time object detection in the sports analytics domain, demonstrating how deep learning can reduce the manual labor typically associated with sports video editing. It supports integration with TensorRT for optimized inference performance, making it a robust solution for developers looking to build automated sports media pipelines.
💡Highlights
- ├─YOLOv4-based object detection
- ├─Automated highlight extraction
- └─TensorRT optimization support
🎯For
- ├─Sports content creators
- ├─Computer vision engineers
- └─AI developers