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首页> 外文会议>Annual IEEE International Conference on Sensing, Communication, and Networking >Passive Crowd Speed Estimation and Head Counting Using WiFi
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Passive Crowd Speed Estimation and Head Counting Using WiFi

机译：使用WiFi进行被动人群速度估计和人数统计

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摘要

In this paper, we propose a framework to sense occupancy attributes of an area, such as speed of a crowd traversing through the area, the total number of people in the area, and the rate of arrival of people into the area, using only the received power measurements (RSSI) of two WiFi links, and without relying on people to carry any device. We first show that the cross-correlation between the two WiFi link measurements and the probability of crossing a link implicitly carry key information about the occupancy attributes and develop a mathematical model to relate these parameters to the occupancy attributes of interest. Based on this, we then propose a system to estimate the occupancy attributes and validate it with 51 experiments in both indoor and outdoor areas, where up to (and including) 20 people walk in the area with different possible speeds, and show that our framework can accurately estimate the occupancy attributes. For instance, our framework achieves a Normalized Mean Square Error (NMSE) of 0.047 (4.7%) when estimating the speed of a crowd, an NMSE of 0.034 (3.4%) when estimating the arrival rate to the area, and a Mean Absolute Error (MAE) of 1.3 when counting the total number of people. We finally run experiments in an aisle in Costco, showing how we can estimate the key attributes of buyers' motion behaviors.
机译：在本文中，我们提出了一个框架来感知区域的占用属性，例如仅通过选择区域的人群穿越区域的速度，区域中的总人数以及人们到达区域的比率。接收两个WiFi链路的功率测量（RSSI），并且无需依靠人员携带任何设备。我们首先显示两个WiFi链路测量值之间的互相关性以及通过链路的概率隐式携带了有关占用属性的关键信息，并建立了数学模型以将这些参数与感兴趣的占用属性相关联。基于此，我们然后提出一个系统来估计占用率属性，并通过室内和室外区域中的51个实验对它进行验证，其中多达20人（包括20个人）以不同的可能速度行走于该区域中，并证明了我们的框架可以准确估计入住人数属性。例如，我们的框架在估算人群速度时达到0.047（4.7 \％）的归一化均方误差（NMSE），在估算到达区域的到达率时达到0.034（3.4 \％）的NMSE，以及均值计算人员总数时的绝对错误（MAE）为1.3。最后，我们在好市多（Costco）的一个过道中进行了实验，展示了如何估算买家运动行为的关键属性。

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