Developed for the Bozeman High School Club Fair, Hand-Pong is an interactive, computer-vision–based game designed to showcase the practical capabilities of modern open-source CV frameworks like Google’s MediaPipe. The project leverages real-time hand-tracking to allow players to control virtual paddles entirely through motion, demonstrating how machine learning and computer vision can be applied beyond research and into interactive entertainment, user-interface design, and accessibility tools.
As part of the ICEMAN road-survey drone project, a YOLO-based computer vision model is required to accurately detect and classify road conditions. While the project is led independently, gathering the large volume of reference images necessary for training will involve multiple contributors for data collection and annotation. This initiative serves as a strong introduction to convolutional neural networks (CNN'S) and practical computer vision workflows, including dataset labeling using tools like YOLO, Hugging Face and CVAT. Participants will gain hands-on experience applying cutting-edge CV techniques to a real engineering challenge with meaningful real-world impact.