Title: Pedestrian characterisation in urban environments combining WiFi and AI

Authors: Antonio Guillen-Perez; Maria-Dolores Cano

Addresses: Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, 30202, Spain ' Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, Cartagena, 30202, Spain

Abstract: Knowing how many people there are in a given scenario offers new possibilities for the development of intelligent services. With this goal in mind, the use of sensors and radio frequency (RF) signals is becoming an interesting alternative to other classic methods such as image processing for counting people. In this paper we present a novel method for counting, characterising, and localising pedestrians in outdoor environments, called intelligent pedestrian characterisation using WiFi (iPCW). iPCW is a passive, device-based sensor system that incorporates artificial intelligence techniques, more specifically, machine learning techniques. Performance evaluation using intensive computer simulations shows that iPCW achieves excellent results, with moving and static pedestrian detection accuracy above 98% and positioning accuracy above 92%.

Keywords: artificial intelligence; intelligent transportation systems; people counting; sensor systems; smart cities; WiFi.

DOI: 10.1504/IJSNET.2021.117964

International Journal of Sensor Networks, 2021 Vol.37 No.1, pp.48 - 60

Received: 28 Jan 2021
Accepted: 28 Jan 2021

Published online: 22 Sep 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article