Authors:
Fatma Bouhlel
1
;
Hazar Mliki
2
and
Mohamed Hammami
3
Affiliations:
1
MIRACL-FSEG, University of Sfax, Faculty of Economics and Management of Sfax, Road Airport Km 4, 3018 Sfax, Tunisia
;
2
MIRACL-ENET’COM, University of Sfax, National School of Electronics and Telecommunications of Sfax, Road Tunis City El Ons, 3018 Sfax, Tunisia
;
3
MIRACL-FS, University of Sfax, Faculty of Sciences of Sfax, Road Sokra Km 3, 3018 Sfax, Tunisia
Keyword(s):
COVID-19, Crowd Behavior, Social Distancing, Crowd Density Estimation, Human Detection, Convolutional Neural Network, UAV.
Abstract:
The outbreak of the COVID-19 and the lack of pharmaceutical intervention increase the spread of COVID-19. Since no vaccine or treatment are yet available, social distancing represents a good strategy to control the propagation of this pandemic and learn to live with it. In this context, we introduce a new approach for crowd behavior analysis from UAV-captured video sequences in order to monitor social distancing. The proposed approach involves two methods: a macroscopic method and a microscopic method. The macroscopic method aims to estimate the crowd density by classifying the aerial frame patches into four categories: Dense, Sparse, Medium and None. However, the microscopic method allows to detect and track humans and then compute the distance between them. The quantitative and qualitative results validate the performance of our methods compared to the state-of-the-art references.