论文标题
基于学习的快速对象跟踪
Learning-based Tracking of Fast Moving Objects
论文作者
论文摘要
跟踪快速移动的对象在视频序列中显示为模糊条纹，对于标准跟踪器来说是一项艰巨的任务，因为对象位置在连续的视频框架和对象的连续视频框架和纹理信息中没有重叠。针对此任务调整的最新方法是基于背景背景和慢速脱毛算法的背景减法。在本文中，我们提出了一种使用最先进的深度学习方法实施的逐节跟踪方法，该方法在现实世界视频序列上执行近实时跟踪。我们实现了物理上合理的FMO序列生成器，以成为我们训练管道的强大基础，并在前景变化方面证明了快速生成器和网络适应性的易于使用。
Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with static background and slow deblurring algorithms. In this paper, we present a tracking-by-segmentation approach implemented using state-of-the-art deep learning methods that performs near-realtime tracking on real-world video sequences. We implemented a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate the ease of fast generator and network adaptation for different FMO scenarios in terms of foreground variations.
