Compensation Tracker：多目标跟踪中丢失目标的再处理
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking
摘要：
检测跟踪是当前最流行的目标跟踪方法之一。然而，它非常依赖于检测器的性能。当检测器存在漏检测行为时，将直接影响跟踪结果。针对MOT2020数据集上实时跟踪模型中跟踪对象丢失的现象进行了分析。在简单传统方法的基础上，我们提出了一种补偿跟踪器，以进一步缓解由于漏检测而导致的丢失跟踪问题。它由运动补偿模块和目标选择模块组成。该方法不仅可以从丢失的目标中重新跟踪丢失的目标，而且不需要额外的网络，以保持实时模型的速度和精度的平衡。我们的方法只需要嵌入到跟踪器中即可工作，而无需重新训练网络。实验表明，补偿跟踪器能有效地提高模型的性能，减少身份切换。在有限的成本下，补偿跟踪器成功地大幅提高了基线跟踪性能，在MOT2020数据集上达到了MOTA的66%和IDF1的67%。
英文摘要：
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.
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。
