论文标题
薪酬跟踪器：重新处理多个目标跟踪的丢失对象
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking
论文作者
论文摘要
通过检测范式跟踪是最受欢迎的对象跟踪方法之一。但是，它非常取决于检测器的性能。当检测器具有缺失检测的行为时，跟踪结果将直接影响。在本文中，我们在MOT2020数据集上的实时跟踪模型中分析了丢失跟踪对象的现象。基于简单和传统的方法，我们提出了一个补偿跟踪器，以进一步减轻由于缺失检测而引起的丢失跟踪问题。它由运动补偿模块和对象选择模块组成。提出的方法不仅可以重新跟踪丢失丢失对象的跟踪对象，而且不需要其他网络以维持实时模型的速度准确性权衡。我们的方法只需要嵌入到跟踪器中才能工作，而无需重新训练网络。实验表明，补偿跟踪器可以有效地提高模型的性能并减少身份转换。凭借有限的成本，薪酬跟踪器成功地提高了基线跟踪性能，并达到了MOTA的66％，而MOT2020数据集中的IDF1的67％。
Tracking by detection paradigm is one of the most popular object tracking methods. However, it is very dependent on the performance of the detector. When the detector has a behavior of missing detection, the tracking result will be directly affected. In this paper, we analyze the phenomenon of the lost tracking object in real-time tracking model on MOT2020 dataset. Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection. It consists of a motion compensation module and an object selection module. The proposed method not only can re-track missing tracking objects from lost objects, but also does not require additional networks so as to maintain speed-accuracy trade-off of the real-time model. Our method only needs to be embedded into the tracker to work without re-training the network. Experiments show that the compensation tracker can efficaciously improve the performance of the model and reduce identity switches. With limited costs, the compensation tracker successfully enhances the baseline tracking performance by a large margin and reaches 66% of MOTA and 67% of IDF1 on MOT2020 dataset.
