Smart City Gnosys

Smart city article details

Title	Countmein: Adaptive Crowd Estimation With Wi-Fi In Smart Cities
ID_Doc	16357
Authors	Solmaz G.; Baranwal P.; Cirillo F.
Year	2022
Published	2022 IEEE International Conference on Pervasive Computing and Communications, PerCom 2022
DOI	http://dx.doi.org/10.1109/PerCom53586.2022.9762354
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. © 2022 IEEE.
Author Keywords	Crowd mobility; machine learning; neural networks

Similar Articles

Id	Similarity	Authors	Title	Published
16660	0.877	Chaaben M.; Bouguila N.; Patterson Z.	Crowd Counting Via Wi-Fi Probe Requests: Integrating Feature Selection And Data Generation	Proceedings - IEEE International Conference on Semantic Computing, ICSC (2025)
16658	0.873	Guo X.; Song K.; Gao M.; Zhai W.; Li Q.; Jeon G.	Crowd Counting In Smart City Via Lightweight Ghost Attention Pyramid Network	Future Generation Computer Systems, 147 (2023)
16111	0.869	Ilyas N.; Shahzad A.; Kim K.	Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, And Performance Evaluation	Sensors (Switzerland), 20, 1 (2020)
14905	0.867	Armacki A.; Milosevic N.; Bajovic D.; Kar S.; Jakovetic D.; Bakhtiarnia A.; Esterle L.; Muscat A.; Festi T.	Communication Efficient Model-Aware Federated Learning For Visual Crowd Counting And Density Estimation In Smart Cities	European Signal Processing Conference (2023)
38979	0.866	Mu M.	Network As A Sensor For Smart Crowd Analysis And Service Improvement	IEEE Network, 37, 2 (2023)
47351	0.865	Zhai W.; Gao M.; Guo X.; Li Q.; Jeon G.	Scale-Context Perceptive Network For Crowd Counting And Localization In Smart City System	IEEE Internet of Things Journal, 10, 21 (2023)
26337	0.864	Pang Y.; Ni Z.; Zhong X.	Federated Learning For Crowd Counting In Smart Surveillance Systems	IEEE Internet of Things Journal, 11, 3 (2024)
16685	0.856	Hu Y.; Liu Y.; Cao G.; Wang J.	Crowdcl: Unsupervised Crowd Counting Network Via Contrastive Learning	IEEE Internet of Things Journal, 12, 12 (2025)
11921	0.856	Xie Z.; Shao F.; Mu B.; Chen H.; Jiang Q.; Lu C.; Ho Y.-S.	Bgdfnet: Bidirectional Gated And Dynamic Fusion Network For Rgb-T Crowd Counting In Smart City System	IEEE Transactions on Instrumentation and Measurement, 73 (2024)
19836	0.856	Janssens R.; Mannens E.; Berkvens R.; Denis S.	Device-Free Crowd Size Estimation Using Wireless Sensing On Subway Platforms	Applied Sciences (Switzerland), 14, 20 (2024)