Counting humans is an essential part of many people-centric applications. In this paper, we propose CrossCount: an accurate deep-learning-based human count estimator that uses a single WiFi link to estimate the human count in an area of interest. The main idea is to depend on the temporal link-blockage pattern as a discriminant feature that is more robust to wireless channel noise than the signal strength, hence delivering a ubiquitous and accurate human counting system. As part of its design, CrossCount addresses a number of deep learning challenges such as class imbalance and training data augmentation for enhancing the model generalizability. Implementation and evaluation of CrossCount in multiple testbeds show that it can achieve a human counting accuracy to within a maximum of 2 persons 100% of the time. This highlights the promise of CrossCount as a ubiquitous crowd estimator with non-labour-intensive data collection from off-the-shelf devices.

中文翻译：

---

CrossCount：一种使用 WiFi 进行无设备人员计数的深度学习系统

对人进行计数是许多以人为中心的应用程序的重要组成部分。在本文中，我们提出了 CrossCount：一种基于深度学习的准确人数估计器，它使用单个 WiFi 链接来估计感兴趣区域的人数。主要思想是将时间链路阻塞模式作为一种判别特征，该特征对无线信道噪声比信号强度更稳健，从而提供无处不在且准确的人员计数系统。作为其设计的一部分，CrossCount 解决了许多深度学习挑战，例如类不平衡和训练数据增强，以增强模型的通用性。CrossCount 在多个测试平台中的实施和评估表明，它可以在 100% 的时间内实现最多 2 人以内的人员计数准确度。