论文标题
一种基于深度学习的基于PIR的多人本地化的方法
A Deep-learning-based Method for PIR-based Multi-person Localization
论文作者
论文摘要
基于Pyroelectric红外（PIR）传感器的无设备定位（DFL）由于其低成本，低功耗和隐私保护的优势引起了很多关注。但是，大多数现有的基于PIR的DFL方法都需要高部署密度才能达到高位置的精度。最近，一些作品提出，可以通过深入分析PIR传感器的模拟输出来降低部署密度。但是，这些方法还不能很好地处理多人场景中的本地化任务。在本文中，我们提出了一个新型的基于PIR的多人本地化的神经网络，该网络适当利用了一系列领域知识。具体而言，提出的网络由两个模块组成：一个用于确定人数，另一个用于确定其位置。同时，人数的模块被进一步设计为两个阶段网络：一个阶段是用于信号分离，而另一个阶段是单人检测。本地化模块还被设计为两个阶段网络：一个阶段是用于信号提取，而另一个阶段是单人定位。通过上述方法，我们成功地将基于PIR的传统方法的部署密度降低了约76 \％，同时保持了本地化精度。
Device-free localization (DFL) based on pyroelectric infrared (PIR) sensors has attracted much attention due to its advantages of low cost, low power consumption, and privacy protection. However, most existing PIR-based DFL methods require high deployment density to achieve high localization accuracy. Recently, a few works proposed that the deployment density can be reduced by deeply analyzing the analog output of PIR sensors. However, these methods can not well handle the localization task in multi-person scenarios yet. In this paper, we propose a novel neural network for PIR-based multi-person localization, which appropriately leverages a series of domain knowledge. Specifically, the proposed network consists of two modules: one is for determining the number of persons and another is for determining their locations. Meanwhile, the module of person counting is further designed as a two-stage network: one stage is for signal separation and another is for single-person detection. The module for localization is also designed as a two-stage network: one stage is for signal extraction and another is for single-person localization. Through the above methods, we succeed to remarkably reduce the deployment density of the traditional PIR-based method by about 76\%, while maintaining the localization accuracy.
