Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors
arxiv(2024)
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要