用WiFi信号估算物体运动，无需摄像头且不受光照影响。
WiFlow: Estimating Optical Flow using WiFi Channel State Information
- 利用WiFi信道状态信息（CSI）替代图像进行光流估计。
- 构建首个基于CSI的光流数据集，验证了模型在不同场景下的有效性。
- 提出三种可选模型，兼顾精度与计算效率，适合隐私敏感场景。
了解场景中物体的位置与运动速度在多个领域至关重要。传统方法依赖摄像头获取数据，但增加摄像头常引发隐私担忧，且图像质量受光照影响显著。本文探索使用WiFi信道状态信息（CSI）替代摄像头图像进行光流估计。提出WiFlow：一种基于CSI的光流估计算法，包含预处理方案及三种在精度与复杂度间权衡的模型架构。此外，构建了首个用于训练和评估基于CSI光流估计器的数据集，实验揭示了该任务的关键设计要素。代码与数据已公开于https://visinf.github.io/wiflow。
原文摘要 · Abstract (English)
Knowing where and how fast objects are moving within a scene is important across various domains. Usually, cameras are used to capture the data necessary for this task, but adding cameras often raises privacy concerns, and the quality of captured frames is heavily influenced by lighting conditions. In this work, we explore using WiFi channel state information (CSI) instead of camera frames for optical flow estimation. We propose WiFlow, a CSI based flow estimator, a preprocessor evaluation for CSI, and three model architectures that offer different trade-offs between accuracy and complexity. Further, we create the first dataset for training and evaluating CSI-based optical flow estimators, and our experiments provide insights into key design elements for this task. Code and data are available at https://visinf.github.io/wiflow.
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