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浓厚兴趣和持续改进的目标检测方法而性能显著提升。多数跟踪方法遵循先检测后跟踪（TBD）范式，盲目信任输入检测结果，却未感知其相关定位不确定性。这种不确定性认知缺失在自动驾驶等安全关键任务中构成隐患——错误检测结果传播至包括MOT在内的下游任务时，可能危及乘员安全。尽管现有概率目标检测方法已预测框周围的定位不确定性，但目前尚无面向自动驾驶的二维MOT研究验证这些估计是否具有足够意义以有效应用于目标跟踪。我们提出UncertaintyTrack，该扩展集合可应用于多种TBD跟踪器，用于处理来自概率目标检测器的定位不确定性估计。在伯克利深度驾驶MOT数据集上的实验表明，将我们的方法与信息量丰富的不确定性估计相结合，可将身份切换次数减少约19%，并将mMOTA提升2-3%。源代码已公开于https://github.com/TRAILab/UncertaintyTrack