Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection

arXiv:2403.01300v1 Announce Type: new
Abstract: RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However, the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically, datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result, multispectral pedestrian detectors show poor generalization ability on examples beyond this …

abstract applications arxiv bias cs.cv datasets detection framework learn novel operations pedestrian safety safety-critical solution statistical type unbiased unsolved