利用自监督学习检测跟踪器错误，以最少人工介入持续收集高质量目标轨迹
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors
AI总结：
本文提出一种结合自监督学习与少量人工介入的混合跟踪框架，通过自动检测跟踪器失败并触发人工重定位，在三个数据集上验证了对小目标、快速运动及遮挡目标的跟踪优势。
摘要：
我们提出一个混合框架，通过将自动目标跟踪器与少量人工输入相结合，持续生成高质量目标轨迹。核心思想是为每个数据集定制一个模块，智能判断目标跟踪器何时失败，从而需要人工介入重新定位目标以继续跟踪。我们的方法利用无标注视频上的自监督学习，学习目标对象的定制化表示，然后用该表示主动监控其被跟踪区域并判断跟踪器何时失败。由于不需要标注数据，我们的方法可应用于新的目标类别。在三个数据集上的实验表明，我们的方法优于现有方法，尤其是对于小尺寸、快速移动或被遮挡的目标。
英文摘要：
We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a module for each dataset to intelligently decide when an object tracker is failing and so humans should be brought in to re-localize an object for continued tracking. Our approach leverages self-supervised learning on unlabeled videos to learn a tailored representation for a target object that is then used to actively monitor its tracked region and decide when the tracker fails. Since labeled data is not needed, our approach can be applied to novel object categories. Experiments on three datasets demonstrate our method outperforms existing approaches, especially for small, fast moving, or occluded objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
