mmYodar+: Robust Human Detection Using mmWave Signals

Yuance Chang, Han Ding, Feng Cao, Cui Zhao, Fei Wang, Ge Wang, Zhi Wang, Wei Xi

Abstract: The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations, such as low lighting conditions, occlusions, and privacy concerns. To address these challenges, we introduce mmYodar+, a novel mmWave-based automatic human detection system. Our system processes mmWave signals to generate a 3-D point cloud, which is then transformed into a 2-D radar image for easier visualization and analysis. To enhance human profiling, we filter the point cloud using biometric information and expand human-related points in the image based on radar angle resolution, incorporating color to improve the differentiation. Additionally, we employ a deep mutual learning (DML) framework, enabling efficient human detection using a lightweight DNN. Experimental results show that mmYodar+ achieves an average precision of 96.29% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human detection.

External IDs:dblp:journals/iotj/ChangDCZWWWX25