Abstract

Crowd counting is of great importance to many applications. Conventional vision-based approaches require line of sight and pose privacy concerns, while most radio-based approaches involve high deployment cost. In this paper, we propose to utilize WiFi channel state information (CSI) to infer crowd count in a device-free way, with only one pair of WiFi transmitter and receiver. The proposed method establishes the statistical relationship between the variation of CSI and the number of people with deep neural networks (DNN) and thereafter estimates the people count according to the real-time CSI through the trained DNN model. Evaluations demonstrate the effectiveness of the method. For the crowd size of 6, the counting error was within 1 person for 100% of the cases. For the crowd size of 34, the counting error was within 1 person for 97.7% of the cases and within 2 persons for 99.3% of the cases.

中文翻译：

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使用WiFi通道状态信息和深度神经网络的无设备人群计数

摘要

人群计数对于许多应用程序非常重要。常规的基于视觉的方法需要视线并涉及隐私问题，而大多数基于无线电的方法都涉及高昂的部署成本。在本文中，我们建议利用WiFi信道状态信息（CSI）来以无设备的方式推断人群数，仅使用一对WiFi发送器和接收器。该方法建立了CSI变化与深度神经网络（DNN）人数之间的统计关系，然后通过训练后的DNN模型根据实时CSI估计人数。评估证明了该方法的有效性。对于6名人群，在100％的情况下，计数误差在1个人以内。对于34岁的人群，计数误差在1人内，为97。