Camera Calibration in Broadcast Basketball Videos of Various Courts

• Main Authors: Hung-Chi Huang, 黃泓棨
• Other Authors: Ming-Sui Lee
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/6q2478

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === Due to a massive growth of basketball videos, the video analysis plays an important role nowadays. Camera calibration is also important since it has been used to preprocess for video analysis. Camera calibration is used to calibrate the court in the frame to the court in the standard basketball court. Because a basketball court is actually a plane, most of the methods use this property to solve a homography to achieve camera calibration. In order to obtain the homography, at least four meaningful points should be detected from the frames of the basketball videos and then used to solve a linear system. In fact, two nonparallel lines result in an intersection point, so the problem of detecting points can turn into line detection. In a basketball video, what we detect are paint lines since they are obvious and often appear in each frame. However, due to various style of modern basketball courts, detecting accurate paint lines are heavily affected. Hence, we propose a camera calibration system which is robust for various style of basketball courts. In this paper, both the dominant color map and the edge map are extracted first, and then scoreboard detection, as well as player detection, are applied. Using above information to design four masks is necessary to remove noisy pixels from the edge map. After that, baseline and sideline are detected and the useless pixels outside the court are filtered out in the edge map. Next, paint line candidates are extracted. Then two constraints and two scores are applied for deciding the final paint lines. Finally, all the lines are refined by means of interpolation and the homography can be calculated for camera calibration. In experiment results, our method is compared with [4] and applied to all NBA courts for evaluation.