People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms

Sensors

Stefano Savazzi¹  Vittorio Rampa¹  Sanaz Kianoush¹  Monica Nicoli² 

[1] National Research Council of Italy (CNR), Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Piazza Leonardo da Vinci 32, 20133 Milano, Italy;Politecnico di Milano, Department of Management, Economics and Industrial Engineering (DIG), Piazza Leonardo da Vinci 32, 20133 Milano, Italy;

关键词: crowd sensing;    MIMO WiFi;    machine learning;    5G;    cloud computing;   

DOI  :  10.3390/s19163450

来源: DOAJ

【 摘 要 】

Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy.

【 授权许可】

Unknown