Bi-Directional People Counting Exploiting Door Open–Close Discrimination Using 4-D Millimeter-Wave Radar
Zhaocheng Yang, Min Zhou, Ping Chu, Qiaoling Cheng, Jianhua Zhou · IEEE Sensors Journal · 2025
The existing radar-based people counting methods have achieved comparatively good detection performance in most scenarios. But in the doorway, opening and closing the door creates interfering trajectories, which leads to the confusion between person and door, and reduces the people counting performance. To address this problem, we propose a bi-direction people counting method exploiting door open-close discrimination using 4D millimeter wave radar. We first detect the presence of door open-close events within the region of interest based on target detection and point cloud height feature extraction. For point clouds captured during door movement, we construct a rectangular frame to separate door target point clouds and perform clustering analysis. Then, we explicitly incorporate door targets into the multi-target tracking process. To achieve more accurate tracking, the data association is decomposed into three stages: type matching, cluster matching, and point assignment within clusters. Besides, different trajectory management strategies are applied for human targets and door targets. Finally, we count people according to the trajectory information and edge region. The experimental results show that the proposed method achieves an average detection accuracy for door open-close events of over 99%, with a counting accuracy of 97.43% for people entering and exiting in passageways. Moreover, it can maintain a counting accuracy of 96% in scenarios with door interference, demonstrating good counting robustness.