Although refinement is commonly used in visual tasks to improve pre-obtained results, it has not been studied for Multiple Object Tracking (MOT) tasks. This could be attributed to two reasons: i) it has not been explored what kinds of errors should — and could — be reduced in MOT refinement; ii) the refinement target, namely, the tracklets, are intertwined and interactive in a 3D spatio-temporal space, and therefore changing one tracklet may affect the others. To tackle these issues, i) we define two types of errors in imperfect tracklets, as Mix-up Error and Cut-off Error, to clarify the refinement goal; ii) we propose a Refining MOT Framework (ReMOT), which first splits imperfect tracklets and then merges the split tracklets with appearance features improved by self-supervised learning. Experiments demonstrate that ReMOT can make significant improvements to state-of-the-art MOT results as powerful post-processing. As a new application, we demonstrate that ReMOT has the potential of being used to assist semi-automatic MOT data annotation and partially release humans from the tedious work.

尽管细化通常用于视觉任务中以改善预先获得的结果，但尚未针对多对象跟踪（MOT）任务进行研究。这可能归因于两个原因：i）尚未探索在MOT改进中应该而且可以减少什么样的错误；ii）优化目标（即小轨迹）在3D时空空间中相互交织和交互，因此更改一个小轨迹可能会影响其他小轨迹。为了解决这些问题，i）我们在不完善的小轨迹中定义两种类型的错误，即混合错误和截止错误，以阐明优化目标；ii）我们提出了一个完善的MOT框架（ReMOT），该框架首先拆分不完善的小轨迹，然后将这些拆分的小轨迹与通过自我监督学习改善的外观特征合并。实验表明，ReMOT作为强大的后处理功能，可以极大地改善最新的MOT结果。作为一个新的应用程序，我们证明ReMOT有潜力被用于辅助半自动MOT数据注释和部分人从繁琐的工作中解脱出来。