ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification
This addresses security applications such as safety inspections and personnel counting by introducing a multimodal approach, though it is incremental as it builds on existing two-stream and contrastive learning techniques.
The paper tackles person re-identification by combining video and WiFi data to overcome limitations like clothing changes and occlusions in image-based methods, resulting in improved accuracy and expanded sensing range in real-world scenarios.
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.