ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification

TL;DR

This paper introduces ViFi-ReID, a multimodal person re-identification system combining video and WiFi signal data, improving accuracy and sensing range by leveraging a two-stream network and contrastive learning.

Contribution

The paper presents a novel two-stream network that fuses visual and WiFi gait data for person ReID, along with a new multimodal dataset and extensive real-world validation.

Findings

Enhanced ReID accuracy across multiple sensors

Effective correlation between visual and WiFi data

Expanded sensing range in real-world scenarios

Abstract

Person re-identification(ReID), as a crucial technology in the field of security, plays a vital role in safety inspections, personnel counting, and more. Most current ReID approaches primarily extract features from images, which are easily affected by objective conditions such as clothing changes and occlusions. In addition to cameras, we leverage widely available routers as sensing devices by capturing gait information from pedestrians through the Channel State Information (CSI) in WiFi signals and contribute a multimodal dataset. We employ a two-stream network to separately process video understanding and signal analysis tasks, and conduct multi-modal fusion and contrastive learning on pedestrian video and WiFi data. Extensive experiments in real-world scenarios demonstrate that our method effectively uncovers the correlations between heterogeneous data, bridges the gap between visual and signal modalities, significantly expands the sensing range, and improves ReID accuracy across multiple sensors.