Tracking Baseball Games with AI: Dynamic Field Analytics

Implementing computer vision in baseball analytics aims to tackle the challenges of tracking high-speed objects across large stadiums, using automated systems to replace outdated manual methods. The process involves setting up a workspace with the Roboflow platform, gathering and curating baseball imagery datasets, and deploying the RF-DETR neural network architecture for real-time detection and tracking. This framework allows for effective processing of video data, facilitating live telemetry and reducing the dependency on cloud infrastructure. By splitting datasets into training, validation, and testing sets, the system ensures robust model performance. Image transformations and enhancements are employed to stabilize the model against varying lighting conditions. The performance metrics of the model, such as mAP@50, Precision, and Recall, indicate strong detection capabilities, though further improvements can be made by expanding the training data and employing techniques like Slicing Aided Hyper Inference (SAHI). The Roboflow Workflows tool aids in creating a visual development environment, connecting detection and tracking blocks to visualize and export final outputs. The integration of RF-DETR and OC-SORT tracking provides a comprehensive solution for automatically extracting baseball metrics and monitoring player and ball movements, offering a scalable approach for sports analytics.