UncertaintyTrack: Exploiting Detection and Localization Uncertainty in
  Multi-Object Tracking
Chang Won Lee Steven L. Waslander
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