用视频和WiFi信号联合识别行人，提升复杂场景下的准确率。
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification
- 双流架构分别处理视频与WiFi信号，融合多模态信息
- 实测在多传感器环境下显著提升行人重识别准确率
- 首次利用WiFi CSI构建行人步态特征，拓展感知范围
行人重识别（ReID）是安防领域的重要技术，广泛应用于安全检查、人员统计等场景。现有方法主要依赖图像特征，易受衣着变化和遮挡影响。本文除使用摄像头外，还利用路由器通过捕捉WiFi信道状态信息（CSI）获取行人步态特征，构建了首个多模态数据集。采用双流网络分别处理视频理解与信号分析任务，并在行人视频与WiFi数据间进行多模态融合与对比学习。大量真实场景实验表明，该方法有效挖掘异构数据间的关联，弥合视觉与信号模态的差距，显著扩展感知范围，提升多传感器环境下的ReID性能。
原文摘要 · Abstract (English)
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.
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