论文标题
基于图形的多相机足球运动员跟踪器
Graph-Based Multi-Camera Soccer Player Tracker
论文作者
论文摘要
该论文提出了一种多摄像机跟踪方法，该方法旨在通过在比赛场周围安装的多个校准摄像机的长期视频录制中跟踪足球运动员。与相机的距离很大，很难在视觉上区分单个玩家，这会对依赖于跟踪物体的外观的传统解决方案的性能产生不利影响。我们的方法着重于邻里玩家之间的单个玩家动态和相互作用，以提高跟踪性能。为了克服在存在校准误差的情况下可靠合并多个相机检测的难度，我们提出了新型跟踪方法，在该方法中，跟踪器直接在来自多个摄像机的原始检测热图上运行。我们的模型接受了使用Google Research Football环境生成的大型合成数据集进行培训，并使用现实世界数据进行了微调，以降低与地面真相准备有关的成本。
The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions relying on the appearance of tracked objects. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance. To overcome the difficulty of reliably merging detections from multiple cameras in the presence of calibration errors, we propose the novel tracking approach, where the tracker operates directly on raw detection heat maps from multiple cameras. Our model is trained on a large synthetic dataset generated using Google Research Football Environment and fine-tuned using real-world data to reduce costs involved with ground truth preparation.
