  The majority of human detection methods rely on the sensor using visible
lights (e.g., RGB cameras) but such sensors are limited in scenarios with
degraded vision conditions. In this paper, we present a multimodal human
detection system that combines portable thermal cameras and single-chip mmWave
radars. To mitigate the noisy detection features caused by the low contrast of
thermal cameras and the multi-path noise of radar point clouds, we propose a
Bayesian feature extractor and a novel uncertainty-guided fusion method that
surpasses a variety of competing methods, either single-modal or multi-modal.
We evaluate the proposed method on real-world data collection and demonstrate
that our approach outperforms the state-of-the-art methods by a large margin.
