Learning Longterm Representations for Person Re-Identification Using Radio Signals
Publication/Creation DateJune 13 2020
DescriptionDemo video for the research paper published in the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
"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."
Date archivedJune 19 2020
Last editedDecember 12 2020
How to cite this entry
Massachusetts Institute Of Technology (MIT), Computer Science and Artificial Intelligence Lab (CSAIL), Dina Katabi, Lijie Fan, Tianhong Li, Rongyao Fang. (June 13 2020). "Learning Longterm Representations for Person Re-Identification Using Radio Signals". Fabric of Digital Life. https://fabricofdigitallife.com/Detail/objects/4612