Toward unlabeled multi-view 3D pedestrian detection by generalizable AI: techniques and performance analysis. (arXiv:2308.04515v1 [cs.CV])

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 …

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