Person tracking and identification using cameras and wi-fi channel state information (CSI) from smartphones: dataset

Abstract

Human sensing, motion trajectory estimation, and identification are crucial to applications such as customer analysis, public safety, smart homes and cities, and access control. In the wake of the global COVID-19 pandemic, the ability to perform contact tracing effectively is vital to limit the spread of infectious diseases. Although vision-based solutions such as facial recognition can potentially scale to millions of people for identification, the privacy implications and laws to banning such a technology limit its applicability in the real world. Other techniques may require installations and maintenance of multiple units, and/or lack long-term re-identification capability. We present a dataset to fuse WiFi Channel State Information (CSI) and camera-based location information for person identification and tracking. While previous works focused on collecting WiFi CSI from stationary transmitters and receivers (laptop, desktop, or router), our WiFi CSI data are generated from a smartphone that is carried while someone is moving. In addition, we collect camera-generated real-world coordinate for each WiFi packet that can serve as ground truth location. The dataset is collected in different environments and with various numbers of persons in the scene at several days to capture real-world variations.