Mission: mmWave Radar Person Identification with RGB Cameras

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person from cameras that are ubiquitously deployed in camera-allowed areas. We envision that cross Vison-RF ReID can significantly enrich mmWave human sensing with a wide spectrum of applications in security surveillance, tracking, and personalized services. Technically, we introduce a novel method for cross-modal similarity estimation that exploits inherent synergies between fine-grained 2D images and coarse-grained 3D Radar point clouds to effectively overcome their modal discrepancy. Through extensive experiments, we demonstrated that our proposed system can achieve 85% top-1 accuracy and 90% top-5 accuracy among 58 volunteers.

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Publication Details

Published
2024-11-04
DOI
https://doi.org/10.1145/3666025.3699340
Citations
10
Primary Topic
Biometric Identification and Security
Type
article
Field-Weighted Citation Impact
3.50
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article

Mission: mmWave Radar Person Identification with RGB Cameras

Tianshun Yao, Ruofeng Liu, Zhimeng Yin, Wenchao Jiang et al.
10 citations
Biometric Identification and Security
3.50
article

Mission: mmWave Radar Person Identification with RGB Cameras

Tianshun Yao, Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Ruixia Shi, Shuai Wang, Luoyu Mei, Shuai Wang
article en
10 citations

Abstract

This paper presents Mission, the first-of-this-kind cross-modal reidentification (ReID) design for mmWave Radar and RGB cameras. Given a person of interest detected by Radar in camera-restricted scenarios, Mission can identify the image of the person from cameras that are ubiquitously deployed in camera-allowed areas. We envision that cross Vison-RF ReID can significantly enrich mmWave human sensing with a wide spectrum of applications in security surveillance, tracking, and personalized services. Technically, we introduce a novel method for cross-modal similarity estimation that exploits inherent synergies between fine-grained 2D images and coarse-grained 3D Radar point clouds to effectively overcome their modal discrepancy. Through extensive experiments, we demonstrated that our proposed system can achieve 85% top-1 accuracy and 90% top-5 accuracy among 58 volunteers.

Singapore University of Technology and Design (SG), City University of Hong Kong (HK), Michigan United (US), Southeast University (CN), Michigan State University (US)
Openalex Percentile: Top 6%
Biometric Identification and Security
3.50
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Mission: mmWave Radar Person Identification with RGB Cameras — Tianshun Yao, Ruofeng Liu, et al. · (2024) | TGRS Research Map | TGRS