@inproceedings{a57191dd125c44fe94a861ecabdf3863,
title = "Poster Abstract: Person Identification Under Heavy Occlusions Using mmWave Radar",
abstract = "We propose mmWave-ocPID, a person identification (PID) method with millimeter-wave radar to identify individuals even when they are heavily occluded by obstacles. We collect a multi-modal dataset comprising mmWave radar point clouds and RGB images obtained from 9 human subjects, with over 180,000 frames for each modality. The mmWave-ocPID prototype employs a novel Neural Network integrated with two augmentation strategies for learning. Our initial experimental results show that mmWave-ocPID can achieve high identification accuracy, even when most of the human body of an individual is occluded in a controlled environment.",
keywords = "millimeter wave radar, occluded conditions, person identification",
author = "Tao Wang and Yang Zhao and Jie Liu",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright is held by the owner/author(s). Publication rights licensed to ACM.; 21st ACM Conference on Embedded Networked Sensors Systems, SenSys 2023 ; Conference date: 13-11-2023 Through 15-11-2023",
year = "2024",
month = apr,
day = "26",
doi = "10.1145/3625687.3628404",
language = "英语",
series = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "540--541",
booktitle = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
}