论文标题
使用RF提示恢复监视视频
Recovering Surveillance Video Using RF Cues
论文作者
论文摘要
视频捕获是最广泛使用的人类感知来源，因为它的直观理解的性质。所需的视频捕获通常需要多种环境条件，例如充足的环境光线，未打开的空间和适当的相机角度。相反，无线测量更加普遍，环境限制更少。在本文中，我们提出了CSI2Video，这是一种新型的跨模式方法，仅利用商业设备的WiFi信号和人类身份信息的来源来实时恢复细粒度的监视视频。具体而言，两个量身定制的深神经网络旨在分别进行跨模式映射和视频生成任务。我们利用基于自动编码器的结构从WiFi帧中提取姿势功能。之后，将两个提取的姿势特征和身份信息合并以生成合成监视视频。我们的解决方案可生成逼真的监视视频，而无需任何昂贵的无线设备，并且具有无处不在，便宜和实时特征。
Video capture is the most extensively utilized human perception source due to its intuitively understandable nature. A desired video capture often requires multiple environmental conditions such as ample ambient-light, unobstructed space, and proper camera angle. In contrast, wireless measurements are more ubiquitous and have fewer environmental constraints. In this paper, we propose CSI2Video, a novel cross-modal method that leverages only WiFi signals from commercial devices and a source of human identity information to recover fine-grained surveillance video in a real-time manner. Specifically, two tailored deep neural networks are designed to conduct cross-modal mapping and video generation tasks respectively. We make use of an auto-encoder-based structure to extract pose features from WiFi frames. Afterward, both extracted pose features and identity information are merged to generate synthetic surveillance video. Our solution generates realistic surveillance videos without any expensive wireless equipment and has ubiquitous, cheap, and real-time characteristics.
