Vision-based Self-training System for Billiards

• Main Authors: Tsai, Hou-Chun, 蔡厚群
• Other Authors: Lee, Suh-Yin
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/31461723153533434588

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 104 === Precisely selecting the aiming angles is undoubtedly a challenge to inexperienced billiard players. However, most studies focusing on self-training systems for billiards are based on ceiling-mounted cameras, which are not practical to general users. As a result, we propose a vision-based self-training system for billiards, named “Improve My Shot,” to help inexperienced billiard players improve their shots. The system can generate top-view simulation table by integrating the side-view images from three or more angles and predict the trajectories of both the cue-ball and the object-ball after collision according to the additional images with the cue-sticks captured from intelligent glasses. The system consists of three main processing modules, including table contour extraction, ball contour extraction and cue-stick direction estimation. Each ball can be located in the real-world coordinates by integrating ball projections from different angles and can be visualized in the top-view simulation table. Finally, with the estimated cue-stick direction, we can predict the trajectories of both the cue-ball and the object-ball after collision. The trajectories are then visualized and presented on the intelligent glasses to provide users with instructions. In the proposed system, Google Glass is adopted for image capturing and result display. After users capture images via their Google Glass, the images are sent to the server for processing and the resulting images with the instruction lines are sent back for display afterwards, which not only enhances the practicability of the system but also helps billiard players improve their shot in more intuitive way.