论文标题
对视觉对象跟踪器的目标条件分割方法的探索
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
论文作者
论文摘要
视觉对象跟踪是在视频中预测目标对象状态的问题。通常，界箱已被用来表示状态，社区已经花费了一系列的努力来产生有效的因果算法，能够定位具有此类表示的目标。随着该领域正朝着二进制分割掩模迈进以更精确地定义对象，在本文中，我们建议广泛探索计算机视觉社区中可用的目标条件条件的分割方法，以便将任何边界框跟踪器转换为细分跟踪器。我们的分析表明，这种方法允许跟踪器在实时执行准化合物时与最近提出的分割跟踪器竞争。
Visual object tracking is the problem of predicting a target object's state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been spent by the community to produce efficient causal algorithms capable of locating targets with such representations. As the field is moving towards binary segmentation masks to define objects more precisely, in this paper we propose to extensively explore target-conditioned segmentation methods available in the computer vision community, in order to transform any bounding-box tracker into a segmentation tracker. Our analysis shows that such methods allow trackers to compete with recently proposed segmentation trackers, while performing quasi real-time.
