基于普适传感器和概率推理的灵巧家庭在线访客检测
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning
摘要：
配备分布式传感器网络的灵巧家庭环境能够通过提供与健康、紧急检测或日常管理相关的服务来帮助人们。这些系统的主干通常依赖于系统跟踪和检测用户在其家中执行的活动的能力。尽管在灵巧家庭中的活动识别领域中不断取得进展，但许多系统做出强有力的潜在假设，即在任何给定时刻家中的居住者的数量总是已知的。估计灵巧家庭中每个时间步的人数仍然是当今的挑战。事实上，与大多数基于计算机视觉技术的（人群）计数解决方案不同，灵巧家庭中考虑的传感器通常非常简单，并且不能单独提供对情况的良好概述。因此，需要对收集到的数据进行融合，以便推断出有用的信息。本文旨在解决这一挑战，并提出了一种概率方法，能够估计在每个时间步的环境中的人数。这种方法分两步工作：首先，使用约束满足问题求解器，基于传感器网络的拓扑结构和传感器激活模式，在这个时间点的环境中存在的人数的估计。然后，隐马尔可夫模型通过考虑与传感器相关的不确定性来细化该估计。使用模拟和真实数据，我们的方法已经在两个不同大小和配置的灵巧家庭上进行了测试和验证，并展示了准确估计居民数量的能力。
英文摘要：
Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system's ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
