CrossCount：一个使用WiFi的无设备人类计数深度学习系统
CrossCount: A Deep Learning System for Device-free Human Counting using WiFi
摘要：
计算人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精确人类计数估计器，它使用单个WiFi链路来估计感兴趣区域的人类计数。其主要思想是依赖于时间链路阻塞模式作为判别特征，其对无线信道噪声比信号强度更鲁棒，因此提供了一个无处不在且准确的人类计数系统。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，以增强模型的泛化能力。CrossCount在多个测试平台中的实施和评估表明，它可以实现100%的时间内最多2人的人类计数准确度。这突出了CrossCount作为一种无处不在的人群估计器的前景，它可以从现成的设备中收集非劳动密集型的数据汇集。
英文摘要：
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.
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。
