Toward unlabeled multi-view 3D pedestrian detection by generalizable AI:
  techniques and performance analysis
João Paulo Lima Diego Thomas Hideaki Uchiyama Veronica Teichrieb
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.