CrowdEstimator：利用多模态数据估算物联网服务的人群规模
CrowdEstimator: Approximating Crowd Sizes with Multi-modal Data for Internet-of-Things Services
摘要：
人群移动性在物联网（IoT）应用中受到关注。本文解决了人群估计问题，并构建了一个物联网服务，以便在不同的系统之间共享人群估计结果。人群估计问题是使用观察到的信息（例如，Wi-Fi数据）。本文利用移动设备广播的Wi-Fi探测请求数据包（简称“Wi-Fi探测”）来解决这个问题。然而，仅使用Wi-Fi探测器来估计人群规模可能由于各种环境不确定性而导致不准确的结果，这可能导致人群高估或低估。此外，由于Wi-Fi信号的覆盖范围是时变的和不可见的，因此无法获得地面实况。本文介绍了辅助传感器，立体相机，收集近地面真理在指定的校准瓶颈点。提出了两种校正算法来解决人群估计问题。其关键思想是基于两种类型的数据模态之间的相关性来校准仅Wi-Fi人群估计。然后，为了在不同利益相关者所需的系统之间共享校准结果，我们的系统与基于FIWARE的物联网平台集成。为了验证拟议的系统，我们在惠灵顿火车站进行了室内试点研究，并在新西兰基督城Re：START购物中心进行了室外试点研究。大规模的试验研究表明，立体相机可以达到最低精度为85%，并提供近地面真实的高精度检测。与仅使用Wi-Fi的方法相比，所提出的校准算法平均将估计误差降低了43.68%。
英文摘要：
Crowd mobility has been paid attention for the Internet-of-things (IoT) applications. This paper addresses the crowd estimation problem and builds an IoT service to share the crowd estimation results across different systems. The crowd estimation problem is to approximate the crowd size in a targeted area using the observed information (e.g., Wi-Fi data). This paper exploits Wi-Fi probe request packets ("Wi-Fi probes" for short) broadcasted by mobile devices to solve this problem. However, using only Wi-Fi probes to estimate the crowd size may result in inaccurate results due to various environmental uncertainties which may lead to crowd overestimation or underestimation. Moreover, the ground-truth is unavailable because the coverage of Wi-Fi signals is time-varying and invisible. This paper introduces auxiliary sensors, stereoscopic cameras, to collect the near ground-truth at a specified calibration choke point. Two calibration algorithms are proposed to solve the crowd estimation problem. The key idea is to calibrate the Wi-Fi-only crowd estimation based on the correlations between the two types of data modalities. Then, to share the calibrated results across systems required by different stakeholders, our system is integrated with the FIWARE-based IoT platform. To verify the proposed system, we have launched an indoor pilot study in the Wellington Railway Station and an outdoor pilot study in the Christchurch Re:START Mall in New Zealand. The large-scale pilot studies show that stereoscopic cameras can reach minimum accuracy of 85\% and high precision detection for providing the near ground-truth. The proposed calibration algorithms reduce estimation errors by 43.68% on average compared to the Wi-Fi-only approach.
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。
