This project implements a human versus computer game of rock-paper-scissors using machine learning and computer vision. Player’s hand gestures are detected using single images with the YOLOv3 object detection system. This provides a generalized detection method which can recognize player moves without the need for a special background or lighting setup. Additionally, past moves are examined in context to predict the most probable next move of the system’s opponent. In this way, the system achieves higher win rates against human opponents than by using a purely random strategy.
Hunter, Nicholas, "Computer Vision Gesture Recognition for Rock Paper Scissors" (2020). Senior Independent Study Theses. Paper 9071.
Artificial Intelligence and Robotics | Software Engineering
machine learning, computer vision, object detection
Bachelor of Arts
Senior Independent Study Thesis Exemplar
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