Multi-object tracking (MOT) methods have seen a significant boost in performance recently, due to strong interest from the research community and steadily improving object detection methods. The majority of tracking methods follow the tracking-by-detection (TBD) paradigm, blindly trust the incoming detections with no sense of their associated localization uncertainty. This lack of uncertainty awareness poses a problem in safety-critical tasks such as autonomous driving where passengers could be put at risk due to erroneous detections that have propagated to downstream tasks, including MOT. While there are existing works in probabilistic object detection that predict the localization uncertainty around the boxes, no work in 2D MOT for autonomous driving has studied whether these estimates are meaningful enough to be leveraged effectively in object tracking. We introduce UncertaintyTrack, a collection of extensions that can be applied to multiple TBD trackers to account for localization uncertainty estimates from probabilistic object detectors. Experiments on the Berkeley Deep Drive MOT dataset show that the combination of our method and informative uncertainty estimates reduces the number of ID switches by around 19\% and improves mMOTA by 2-3%. The source code is available at https://github.com/TRAILab/UncertaintyTrack

翻译：多目标跟踪（MOT）方法近期因研究社区的强烈兴趣和不断改进的目标检测方法而取得了显著的性能提升。大多数跟踪方法遵循“先检测后跟踪”（TBD）范式，盲目信任输入的检测结果，对其相关的定位不确定性毫无感知。这种不确定性感知的缺失在安全关键任务（如自动驾驶）中构成问题，因为错误检测结果传播至下游任务（包括MOT）可能危及乘客安全。尽管现有概率目标检测研究已可预测包围框周围的定位不确定性，但自动驾驶领域的二维MOT尚无工作探究这些估计值是否足够有意义以有效用于目标跟踪。我们提出UncertaintyTrack——一组可应用于多种TBD跟踪器的扩展模块，用于处理来自概率目标检测器的定位不确定性估计。在Berkeley Deep Drive MOT数据集上的实验表明，结合我们的方法与信息性不确定性估计，可将ID切换次数减少约19%，并将mMOTA提升2-3%。源代码已开源：https://github.com/TRAILab/UncertaintyTrack