论文标题
CrossCount：使用WiFi的深度学习系统，用于无设备的人类计数
CrossCount: A Deep Learning System for Device-free Human Counting using WiFi
论文作者
论文摘要
计数人是许多以人为本应用的重要组成部分。在本文中，我们提出了CrossCount：一种精确的基于深度学习的人数估计器，该估计值使用单个WiFi链接来估计感兴趣领域的人类计数。主要思想是依靠时间链接阻滞模式作为一种判别特征，它对无线通道噪声比信号强度更强大，因此提供无处不在，准确的人类计数系统。作为设计的一部分，CrossCount解决了许多深度学习挑战，例如类不平衡和培训数据增强，以增强模型的推广性。多个测试床中交叉扣的实施和评估表明，它可以达到人类计数的准确性，最多可以在2人的时间内100％。这凸显了CrossCount作为无处不在的人群估计器的希望，并从现成的设备中收集了非劳动密集型数据。
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.
