In this article, we propose a novel and effective system based on multiple cameras to extract the events for soccer matches. A precise ontological definition of the soccer events is still an open point. According to our definition, the events include the free kick, corner kick, penalty kick and the goal, because they are the representative shots for the audience to watch. The events are very important for highlights selection and sport data analysis. At present, the events including the ball and players information are selected and labeled manually from the images, which is a big workload for the staffs. Addressing this problem, our system provides an automatic extraction of the events. For soccer videos, our system first uses the local-based deep neural network for the ball and player detection from the input images. Then, we handle with the ball and player bounding boxes separately. For players, a player can be labeled as one of the three types: two teams or the referee, and a novel unsupervised U-encoder is designed for the player labeling. For soccer ball, the application of multiple cameras allows us to refine the ball detection results. We can get the world coordinate of ball according to the camera parameters and then rebuild the ball trajectory and the court in a top view. Based on the reconstructed map, we get the soccer events by motion analysis of ball trajectory and then apply the ball location and player classification results to display the events for each camera. The test results on real videos of European soccer league show the good detection and labeling performance of our system. We find all the events in the test videos. Our proposed system can deal with many complex cases such as occlusion and pose variation that happen frequently in real applications.

中文翻译：

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基于多摄像机的自动事件提取系统，用于真实足球视频

在本文中，我们提出了一种新颖有效的系统，该系统基于多个摄像机来提取足球比赛的事件。足球赛事的精确本体定义仍然是一个开放点。根据我们的定义，这些事件包括任意球，角球，罚球和进球，因为它们是观众观看的代表镜头。这些事件对于精彩集锦选择和运动数据分析非常重要。目前，从图像中手动选择并标记包括球和运动员信息的事件，这对于员工来说是很大的工作量。为了解决这个问题，我们的系统提供了事件的自动提取。对于足球视频，我们的系统首先使用基于本地的深度神经网络从输入图像中检测球和球员。然后，我们分别处理球和球员边界框。对于运动员，可以将运动员标记为三种类型之一：两支球队或裁判，并且为运动员标记设计了一种新颖的无监督U编码器。对于足球，多个摄像头的应用使我们能够完善球的检测结果。我们可以根据相机参数获取球的世界坐标，然后在顶视图中重建球的轨迹和球场。基于重建的地图，我们通过对球轨迹的运动分析来获取足球事件，然后应用球位置和球员分类结果来显示每个摄像机的事件。欧洲足球联赛真实视频的测试结果显示了我们系统的良好检测和标记性能。我们在测试视频中找到了所有事件。