毫米波雷达通过呼吸微动信号实现密集静态人群精准计数
mmCounter: Static People Counting in Dense Indoor Scenarios Using mmWave Radar
- 提取呼吸和微动作的超低频信号，结合空间信息区分个体
- 在熟悉环境达87%准确率，未见环境仍保持60%准确率
- 适合高密度室内场景的无感人数统计，如会议室、地铁站
毫米波雷达在密集静态人群场景中难以检测或计数，受限于空间分辨率且依赖运动特征。我们提出mmCounter，可在每平方米最多三人的情况下准确计数静态人员。该方法通过提取低于1赫兹的呼吸及微小体动信号，结合新型信号处理技术，将这些细微信号从背景噪声和静止物体中分离，并映射到具体个体。与已有呼吸频率估计研究不同，mmCounter无需预先知道人数。在多种环境下的评估显示，其在熟悉环境中平均F1得分为87%，均方误差为0.6；在未知环境中平均F1得分为60%，均方误差为1.1。系统可对三平方米内多达七人（无并排间距，前后仅一米）进行有效计数。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。