  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.
