CrossCount: A Deep Learning System for Device-Free Human Counting Using WiFi
Open accessAbstract
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 nonlabor-intensive data collection from off-the-shelf devices.
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Limited data collection can create an imbalanced data distribution for different count classes, limiting the availability of large training data for deep learning models.
“a limited data collection can create an imbalanced data distribution for different count classes, which limits the availability of large amounts of training data for deep learning models (Ibrahim etal. 2019).”
From A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning (2025) · cites this paper as Ibrahim et al. 2019
Previous studies evaluated systems with limited numbers of people: up to 10 people (Choi et al. 2022; Ibrahim et al. 2019), or up to 5 people in an indoor environment (Liu et al. 2019).
“Previous studies on automatic people counting evaluated their systems with limited numbers of people, such as up to 10 people (Choi etal. 2022; Ibrahim etal. 2019), or up to 5 people in an indoor environment (Liu etal. 2019).”
From A low-cost automatic people-counting system at bus stops using Wi-Fi probe requests and deep learning (2025) · cites this paper as Ibrahim et al. 2019
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- Authors
- Ibrahim, O. T., Gomaa, W., & Youssef, M.
- Affiliations
- Zagazig University (EG)
- Egypt-Japan University of Science and Technology (EG)
- Alexandria University (EG)
- Venue
- IEEE Sensors Journal
- Published
- 2019-11-01
- Volume
- 19
- Issue
- 21
- Pages
- 9921-9928
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2956800107
- Cited by
- 61
- References
- 26
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 4 · 2023: 11 · 2022: 11 · 2021: 15 · 2020: 11
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ph.D. Scholarship from the Missions Department, Ministry of Higher Education, Government of Egypt
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- WiFi sensing, link blockage, crowd counting, class imbalance, data augmentation, deep learning, ubiquitous sensing
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