We unveil how generalizable AI can be used to improve multi-view 3D
pedestrian detection in unlabeled target scenes. One way to increase
generalization to new scenes is to automatically label target data, which can
then be used for training a detector model. In this context, we investigate two
approaches for automatically labeling target data: pseudo-labeling using a
supervised detector and automatic labeling using an untrained detector (that
can be applied out of the box without any training). We adopt a training
framework for optimizing detector models using automatic labeling procedures.
This framework encompasses different training sets/modes and multi-round
automatic labeling strategies. We conduct our analyses on the
publicly-available WILDTRACK and MultiviewX datasets. We show that, by using
the automatic labeling approach based on an untrained detector, we can obtain
superior results than directly using the untrained detector or a detector
trained with an existing labeled source dataset. It achieved a MODA about 4%
and 1% better than the best existing unlabeled method when using WILDTRACK and
MultiviewX as target datasets, respectively.

利用通用化人工智能改进无标签目标场景的多视角 3D 行人检测，通过使用未经训练的检测器的自动标注方法，可以获得比直接使用未经训练检测器或使用现有标记的源数据集训练的检测器更好的结果。在使用 WILDTRACK 和 MultiviewX 作为目标数据集时，MODA 比现有的无标签方法分别提高了 4% 和 1%。