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We unveil how generalizable AI can be used to improve multi-view 3Dpedestrian detection in unlabeled target scenes. One way to increasegeneralization to new scenes is to automatically label target data, which canthen be used for training a detector model. In this context, we investigate twoapproaches for automatically labeling target data: pseudo-labeling using asupervised detector and automatic labeling using an untrained detector (thatcan be applied out of the box without any training). We adopt a trainingframework for optimizing detector models using automatic labeling procedures.This framework encompasses different training sets/modes and multi-roundautomatic labeling strategies. We conduct our analyses on thepublicly-available WILDTRACK and MultiviewX datasets. We show that, by usingthe automatic labeling approach based on an untrained detector, we can obtainsuperior results than directly using the untrained detector or a detectortrained with an existing labeled source dataset. It achieved a MODA about 4%and 1% better than the best existing unlabeled method when using WILDTRACK andMultiviewX as target datasets, respectively.

Toward unlabeled multi-view 3D pedestrian detection by generalizable AI: techniques and performance analysis