The detection and localization of the ball in sport videos is crucial to better understand events and actions occurring in those sports. Despite recent advances in the field of object detection, the automatic detection of balls remains a challenging task due to the unsteady nature of balls in images. In this paper, we address the detection of small, fast-moving balls in sport video data and introduce a real-time ball detection approach based on the YOLOv3 object detection model. We apply specific adjustments to the network architecture and training process in order to enhance the detection accuracy and speed: We facilitate an efficient integration of motion information, avoiding a complex modification of the network architecture. Furthermore, we present a customized detection approach that is designed to primarily focus on the detection of small objects. We integrate domain-specific knowledge to adapt image pre-processing and a data augmentation strategy that takes advantage of the special features of balls in images in order to improve the generalization ability of the detection network. We demonstrate that the general trade-off between detection speed and accuracy of the YOLOv3 model can be enhanced in consideration of domain-specific prior knowledge.

中文翻译：

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视频中运动球检测的速度和精度折衷的增强—实时查找快速移动的小物体

运动视频中球的检测和定位对于更好地了解这些运动中发生的事件和动作至关重要。尽管在物体检测领域中有最新进展，但是由于图像中球的不稳定特性，自动检测球仍然是一项艰巨的任务。在本文中，我们解决了体育视频数据中小的快速移动球的检测问题，并介绍了一种基于YOLOv3对象检测模型的实时球检测方法。我们对网络体系结构和培训过程进行了特定的调整，以提高检测的准确性和速度：我们促进运动信息的有效集成，避免了对网络体系结构的复杂修改。此外，我们提供了一种定制的检测方法，该方法主要用于检测小物体。我们集成了特定领域的知识以适应图像预处理和利用图像中球的特殊功能的数据增强策略，从而提高检测网络的泛化能力。我们证明，考虑到特定领域的先验知识，可以提高YOLOv3模型的检测速度和准确性之间的一般权衡。