mmWave radars struggle to detect or count individuals in dense, static (non-moving) groups due to limitations in spatial resolution and reliance on movement for detection. We present mmCounter, which accurately counts static people in dense indoor spaces (up to three people per square meter). mmCounter achieves this by extracting ultra-low frequency (< 1 Hz) signals, primarily from breathing and micro-scale body movements such as slight torso shifts, and applying novel signal processing techniques to differentiate these subtle signals from background noise and nearby static objects. Our problem differs significantly from existing studies on breathing rate estimation, which assume the number of people is known a priori. In contrast, mmCounter utilizes a novel multi-stage signal processing pipeline to extract relevant low-frequency sources along with their spatial information and map these sources to individual people, enabling accurate counting. Extensive evaluations in various environments demonstrate that mmCounter delivers an 87% average F1 score and 0.6 mean absolute error in familiar environments, and a 60% average F1 score and 1.1 mean absolute error in previously untested environments. It can count up to seven individuals in a three square meter space, such that there is no side-by-side spacing and only a one-meter front-to-back distance.

翻译：由于空间分辨率的限制以及对运动检测的依赖，毫米波雷达在密集静态（非移动）人群中难以检测或计数个体。本文提出mmCounter，能够精确统计密集室内空间（最高可达每平方米三人）中的静态人员数量。mmCounter通过提取超低频（<1 Hz）信号——主要来源于呼吸及微尺度身体运动（如躯干轻微偏移），并应用新颖的信号处理技术将这些微弱信号与背景噪声及邻近静态物体区分开来，从而实现准确计数。本研究与现有呼吸频率估计研究存在显著差异，后者通常假设人数已知。相比之下，mmCounter采用创新的多级信号处理流程，提取相关低频信号源及其空间信息，并将这些信号源映射至个体人员，从而实现精确计数。在多种环境中的广泛评估表明，mmCounter在熟悉环境中平均F1分数达87%、平均绝对误差为0.6；在未测试环境中平均F1分数为60%、平均绝对误差为1.1。该系统可在三平方米空间内（人员无并排间距且前后距离仅一米）对最多七人进行准确计数。