Crowd Monitoring Using Machine Learning

ABSTRACT

Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, public safety, crowd abnormalities, visual surveillance and urban planning. The purpose of crowd density analysis is to calculate the concentration of the crowd in the videos of observers. The job of detecting a face in the crowd is complicated due to the variability present in human faces including color, pose, expression, position, orientation, and illumination. This paper proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.