IEEE Trans Pattern Anal Mach Intell. 2021 Aug;43(8):2809-2821. doi: 10.1109/TPAMI.2020.2973571. Epub 2021 Jul 1.
Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
智能家居配备匿名二进制传感器提供了一种低成本、不引人注目的解决方案，为活动感知应用提供支持，例如建筑自动化、健康监测、行为干预和家庭安全。然而，当多个居民居住在智能家居中时，将传感器事件与相应的居民相关联可能会带来重大挑战。以前在智能家居中进行多居民跟踪的方法依赖于额外的信息，例如传感器布局、平面图和注释数据，这些信息在实际中可能不可用或不方便获取。为了解决现实生活部署中的这些挑战，我们引入了 sMRT 算法，该算法无需依赖真实标记的传感器数据或其他额外信息，即可同时跟踪每个居民的位置并估计智能家居中的居民数量。我们使用在真实环境中记录的两个智能家居数据集来评估我们方法的性能，并将 sMRT 与另外两种依赖传感器布局和真实标记传感器数据的方法进行比较。
