This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

中文翻译：

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使用mmWave传感器进行室内检测和跟踪人员

本文提出了一种使用毫米波（mmWave）雷达传感器的新型室内人员检测和跟踪系统。首先，提出了一种用于人员检测和跟踪的系统方法-一种用于去除mmWave雷达数据的静态点的静态杂波去除算法。两种有效的聚类算法用于对场景中的人物进行聚类和识别。具有数据关联性的递归卡尔曼滤波器跟踪算法用于同时跟踪多个人。其次，基于本文提出的算法，设计了一种快速的室内人员检测与跟踪系统。该方法轻巧，足以实现可伸缩性和可移植性，我们可以在Raspberry Pi 4上实时执行该方法。最后，通过与德州仪器（TI）系统进行比较，对所提出的方法进行了验证。拟议的系统的实验精度范围从一个人的98％（由错误分类错误计算）到五个人的65％。位置1、2和3的平均位置误差分别为0.2992米，0.3271米和0.3171米。相比之下，德州仪器（TI）系统的实验精度从一个人的96％到五个人的45％不等。位置1、2和3的平均位置误差分别为0.3283米，0.3116米和0.3343米。从跟踪精度，计算时间和可伸缩性方面证明了该方法的优势。德州仪器（TI）系统的实验精度从一个人的96％到五个人的45％不等。位置1、2和3的平均位置误差分别为0.3283米，0.3116米和0.3343米。从跟踪精度，计算时间和可伸缩性方面证明了该方法的优势。德州仪器（TI）系统的实验精度从一个人的96％到五个人的45％不等。位置1、2和3的平均位置误差分别为0.3283米，0.3116米和0.3343米。从跟踪精度，计算时间和可伸缩性方面证明了该方法的优势。