CrossCount: A Deep Learning System for Device-free Human Counting using WiFi
This provides a device-free, accurate crowd estimation solution for people-centric applications, though it is incremental as it builds on existing WiFi sensing methods.
The paper tackles the problem of counting humans using WiFi signals by proposing CrossCount, a deep learning system that estimates human count from temporal link-blockage patterns, achieving 100% accuracy within a maximum error of 2 persons.
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.