Learning Longterm Representations for Person Re-Identification Using Radio Signals

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.

Toward a Wearable Crowdsource System to Monitor Respiratory Symptoms for Pandemic Early Warning

“…Data sources can be diverse and not limited to wearables. Researchers recently use electronic patient-reported outcomes [11] and radio-based person identification information [12] to enhance the early respiratory monitoring system. Nevertheless, we would like to highlight that accuracy is not the only consideration for a practical system.…”

WiImage: Fine-grained Human Imaging from Commodity WiFi

“…Human bodies and human motions will induce modulated RF signal fluctuation remarkably. Past works leverage this phenomenon to sense human gestures, activities and vital signs [2], [15], [22], [28], [29], [30], [31], [32], [33], [34]. The main difference between past proposals is to use various RF signals to conduct the sensing.…”

A New Automotive Radar 4D Point Clouds Detector by Using Deep Learning

“…For the data-driven methods, unlike other sensors such as Lidar and camera, the labels of radar data are usually hard to obtain. [14] collects groundtruth labels by using RGB video cameras colocated with each radio. [13] collects labeled radar points by calibrating the radar data in an anechoic chamber with a known point-target (corner reflector).…”