CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities

Gürkan Solmaz

Abstract:

—The widespread use of pervasive sensing technologies such as wireless sensors and street cameras allows the deployment of crowd estimation solutions in smart cities. However,
existing Wi-Fi-based systems do not provide highly accurate
crowd size estimation. Furthermore, these systems do not adapt
to the dynamic changes in-the-wild, such as unexpected crowd
gatherings. This paper presents a new adaptive machine learning
system, called CountMeIn, to address the crowd estimation
problem using polynomial regression and neural networks. The
approach transfers the calibration task from cameras to machine
learning after a short training with people counting from stereoscopic cameras, Wi-Fi probe packets, and temporal features.
After the training, CountMeIn calibrates Wi-Fi using the trained
model and maintains high accuracy for a longer duration without
cameras. We test the approach in our pilot study in Gold Coast,
Australia, for about five months. CountMeIn achieves 44% and
72% error reductions in minutely and hourly crowd estimations
compared to the state-of-the-art methods.