Learning Longterm Representations for Person Re-Identification Using Radio Signals
Name
2004.01091.pdf
Description
Submitted version
Size
7.47 MB
Format
Adobe PDF
Checksum (MD5)
967586e24b39cdbe85822378187e508a
Author(s)
Fan, Lijie
Li, Tianhong
Fang, Rongyao
Hristov, Rumen H.
Yuan, Yuan
Katabi, Dina
Date Issued
August 2020
Journal
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Fan, Lijie et al. "Learning Longterm Representations for Person Re-Identification Using Radio Signals." 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020, Seattle, Washington, Institute of Electrical and Electronics Engineers, August 2020. © 2020 IEEE
Version
Original manuscript
Abstract
Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. Existing ReID methods rely on images or videos collected using RGB cameras. They extract appearance features like clothes, shoes, hair, etc. Such features, however, can change drastically from one day to the next, leading to inability to identify people over extended time periods. In this paper, we introduce RF-ReID, a novel approach that harnesses radio frequency (RF) signals for longterm person ReID. RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape. We evaluate the performance of RF-ReID on longitudinal datasets that span days and weeks, where the person may wear different clothes across days. Our experiments demonstrate that RF-ReID outperforms state-of-the-art RGB-based ReID approaches for long term person ReID. Our results also reveal two interesting features: First since RF signals work in the presence of occlusions and poor lighting, RF-ReID allows for person ReID in such scenarios. Second, unlike photos and videos which reveal personal and private information, RF signals are more privacy-preserving, and hence can help extend person ReID to privacy-concerned domains, like healthcare.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr42600.2020.01071
