Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion
This addresses the problem of robust human detection for applications in low-visibility scenarios, representing a strong specific gain rather than a foundational advancement.
The paper tackles human detection in degraded vision conditions by fusing thermal cameras and mmWave radars, proposing a Bayesian feature extractor and uncertainty-guided fusion method that outperforms state-of-the-art methods by a large margin.
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.