Fitwi, A.; Chen, Y.; Sun, H.; Harrod, R. Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera. Computers2021, 10, 143.
Fitwi, A.; Chen, Y.; Sun, H.; Harrod, R. Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera. Computers 2021, 10, 143.
Fitwi, A.; Chen, Y.; Sun, H.; Harrod, R. Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera. Computers2021, 10, 143.
Fitwi, A.; Chen, Y.; Sun, H.; Harrod, R. Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera. Computers 2021, 10, 143.
Abstract
For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.
Keywords
Area Estimation, Crowd Management, COVID-19, Edge Camera, Interpersonal Distance, Social Distancing.
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
