2023/07/07 by Kaiwen Cai, Qiyue Xia, Cai, Kaiwen +7
Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #Advanced SAR Imaging Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Target Detection Methodologies #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2307.03623
openalex publication_date 2023/07/07 · openalex created_date 2023/07/11 · openalex updated_date 2026/07/28
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.