论文标题
仅使用合成训练数据从现实世界媒体来源识别和提取足球功能
Identifying and Extracting Football Features from Real-World Media Sources using Only Synthetic Training Data
论文作者
论文摘要
用于训练机器学习算法的现实世界图像通常是非结构化的且不一致的。分析和标记这些图像的过程可能是昂贵的，并且容易出错（也是可用性，差距和法律难题）。但是，正如我们在本文中所证明的那样，与现实世界中无法区分的准确图形图像具有在机器学习范式中具有许多好处。一个这样的例子是来自广播服务（电视和其他流媒体来源）的足球数据。足球比赛通常是从多个来源（相机和电话）和决议中记录的，更不用说，视觉细节和其他人工制品（例如模糊，风化和照明条件）的阻塞，这使得难以准确识别功能。我们演示了一种能够使用生成的标记和结构化图像来克服这些局限性的方法。生成的图像能够模拟多种视图和条件（包括噪声和模糊），这些视图和条件可能仅在现实世界中偶尔出现，并且使机器学习算法很难“应对”真实数据中这些不可预见的问题。这种方法使我们能够迅速训练并准备一个可靠的解决方案，从现实世界足球比赛来源中准确提取功能（例如，空间位置，球场上的标记，球员位置，球位置和摄像头FOV），以用于分析目的。
Real-world images used for training machine learning algorithms are often unstructured and inconsistent. The process of analysing and tagging these images can be costly and error prone (also availability, gaps and legal conundrums). However, as we demonstrate in this article, the potential to generate accurate graphical images that are indistinguishable from real-world sources has a multitude of benefits in machine learning paradigms. One such example of this is football data from broadcast services (television and other streaming media sources). The football games are usually recorded from multiple sources (cameras and phones) and resolutions, not to mention, occlusion of visual details and other artefacts (like blurring, weathering and lighting conditions) which make it difficult to accurately identify features. We demonstrate an approach which is able to overcome these limitations using generated tagged and structured images. The generated images are able to simulate a variety views and conditions (including noise and blurring) which may only occur sporadically in real-world data and make it difficult for machine learning algorithm to 'cope' with these unforeseen problems in real-data. This approach enables us to rapidly train and prepare a robust solution that accurately extracts features (e.g., spacial locations, markers on the pitch, player positions, ball location and camera FOV) from real-world football match sources for analytical purposes.
