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Image-based camera tracking for athletics

机译:基于图像的田径运动相机跟踪

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When broadcasting sports events it is useful to be able to place virtual 3D annotations on the ground, to indicate things such as world record lines and distances. This requires the camera pose to be estimated in real time, so that the graphics can be rendered to match the camera view. Whilst camera calibration data can be obtained by using sensors on the camera mount and lens, such sensors can be impractical or expensive to install, and often the broadcaster only has access to the video feed itself. An image-based method of tracking the camera movement is thus the only practical approach in many situations. This paper reviews past work on image-based camera tracking, and presents the method we have developed. Our approach uses a method based on randomized trees for initial feature identification, and a KLT-based tracker to track features from frame to frame. Results of our system are presented on a selection of material representative of typical broadcast athletics, and the performance benefits of the approach we have taken are compared to a simple KLT-based tracker.
机译:广播体育赛事时,能够在地面上放置虚拟3D注释以指示诸如世界记录线和距离之类的东西很有用。这要求实时估计摄像机的姿势,以便可以渲染图形以匹配摄像机的视图。尽管可以通过使用摄像机安装座和镜头上的传感器来获取摄像机校准数据,但是安装此类传感器可能不切实际或昂贵,并且通常广播公司只能访问视频源本身。因此,在许多情况下,基于图像的跟踪摄像机运动的方法是唯一实用的方法。本文回顾了基于图像的摄像机跟踪的过去工作,并介绍了我们开发的方法。我们的方法使用基于随机树的方法进行初始特征识别,并使用基于KLT的跟踪器从一帧到另一帧跟踪特征。我们的系统结果显示在典型的广播体育节目的材料代表中,并将我们所采用的方法的性能优势与基于KLT的简单跟踪器进行了比较。

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