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

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...

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Bibliographic Details
Published in2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 26774 - 26783
Main Authors Kim, Taeheon, Shin, Sebin, Yu, Youngjoon, Kim, Hak Gu, Ro, Yong Man
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.06.2024
Subjects
Codes
Computer vision
Correlation
Detectors
Multiplexing
Pedestrians
Training
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Summary: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 1 1 R⋆T⋆ refers to the visibility (O/X) in each modality. Generally, ROTO refers to daytime images, and RXTO refers to nighttime images. ROTX refers to daytime images in obscured situations. (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 statistical correlation, such as ROTX data. To address this problem, we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover, we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST, CVC-14, FLIR) and the new ROTX-MP. Our code and dataset are available at: https://github.com/ssbin0914/Causal-Mode-Multiplexer.git.
ISSN:2575-7075
DOI:10.1109/CVPR52733.2024.02529