Poster Abstract: Person Identification Under Heavy Occlusions Using mmWave Radar
Authors: 
Tao Wang, Yang Zhao, Jie LiuAuthors Info & Claims
Tao Wang, Yang Zhao, Jie LiuAuthors Info & ClaimsPages 540 - 541
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.
References
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Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. 2022. Open-Set Recognition: A Good Closed-Set Classifier is All You Need. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. OpenReview.net.
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Information & Contributors
Information
Published In
November 2023
574 pages
ISBN:9798400704147
DOI:10.1145/3625687
- General Chair:
- Rasit Eskicioglu,
- Program Chair:
- Polly Huang,
- Program Co-chair:
- Neal Patwari
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 April 2024
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- Short-paper
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SenSys '23
SenSys '23: 21st ACM Conference on Embedded Networked Sensor Systems
November 12 - 17, 2023
Istanbul, Turkiye
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Overall Acceptance Rate 198 of 990 submissions, 20%
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