The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Satu kunci untuk melaksanakan bandar pintar ialah membiarkan ruang pintar mengetahui di mana dan bilangan orang. Kaedah visual ialah skim untuk mengenali orang yang mempunyai ketepatan yang tinggi, tetapi timbul kebimbangan mengenai potensi kebocoran privasi dan ketidakterimaan pengguna. Selain itu, berfungsi dalam persekitaran terhad semasa kecemasan juga harus dipertimbangkan. Kami mencadangkan sistem pengiraan dan pengesanan orang masa nyata berdasarkan radar gelombang milimeter (mmWave) sebagai alternatif kepada penyelesaian optik di restoran. Kaedah yang dicadangkan terdiri daripada empat prosedur utama. Mula-mula, tangkap awan titik halangan dan jananya menggunakan radar mmWave kos rendah, komersial di luar rak (COTS). Seterusnya, kumpulkan titik individu dengan sifat yang serupa. Kemudian orang yang sama dalam bingkai berjujukan akan dikaitkan dengan algoritma penjejakan. Akhir sekali, orang yang dianggarkan akan dikira, dijejaki dan ditunjukkan dalam bingkai seterusnya. Keputusan eksperimen menunjukkan bahawa sistem cadangan kami memberikan ralat kedudukan median sebanyak 0.17 m dan ketepatan pengiraan 83.5% untuk sepuluh orang dalam dalam pelbagai senario dalam persekitaran restoran sebenar. Di samping itu, anggaran masa nyata dan visualisasi nombor dan kedudukan orang ramai menunjukkan keupayaan berpotensi untuk membantu mencegah orang ramai semasa pandemik Covid-19 dan menganalisis corak lawatan pelanggan untuk pengurusan yang cekap dan pemasaran sasaran.
Shenglei LI
Waseda University
Reiko HISHIYAMA
Waseda University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Shenglei LI, Reiko HISHIYAMA, "Counting and Tracking People to Avoid from Crowded in a Restaurant Using mmWave Radar" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 6, pp. 1142-1154, June 2023, doi: 10.1587/transinf.2022EDP7145.
Abstract: One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7145/_p
Salinan
@ARTICLE{e106-d_6_1142,
author={Shenglei LI, Reiko HISHIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Counting and Tracking People to Avoid from Crowded in a Restaurant Using mmWave Radar},
year={2023},
volume={E106-D},
number={6},
pages={1142-1154},
abstract={One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.},
keywords={},
doi={10.1587/transinf.2022EDP7145},
ISSN={1745-1361},
month={June},}
Salinan
TY - JOUR
TI - Counting and Tracking People to Avoid from Crowded in a Restaurant Using mmWave Radar
T2 - IEICE TRANSACTIONS on Information
SP - 1142
EP - 1154
AU - Shenglei LI
AU - Reiko HISHIYAMA
PY - 2023
DO - 10.1587/transinf.2022EDP7145
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2023
AB - One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.
ER -