Crowd Behavior Analysis based on Convolutional Neural Network: Social Distancing Control COVID-19

Bouhlel, F.; Mliki, H.; Hammami, M.

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.