Monitoring social distancing through human detection for preventing/reducing COVID spread

Abstract

COVID-19 is a severe epidemic that has put the world in a global crisis. Over 42 Million people are infected, and 1.14 Million deaths are reported worldwide as on Oct 23, 2020. A deeper understanding of the epidemic suggests that a person’s negligence can cause widespread harm that would be difficult to negate. Since no vaccine is yet developed, social distancing must be practiced to detain COVID-19 spread. Therefore, we aim to develop a framework that tracks humans for monitoring the social distancing being practiced. To accomplish this objective of social distance monitoring, an algorithm is developed using object detection method. Here, CNN based object detector is explored to detect human presence. The object detector’s output is used for calculating distances between each pair of humans detected. This approach of social distancing algorithm will red mark the persons who are getting closer than a permissible limit. Experimental results prove that CNN based object detectors with our proposed social distancing algorithm exhibit promising outcomes for monitoring social distancing in public areas.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

“…Therefore, computer vision and artificial intelligence-based automatic face mask detection and physical distance measurement have been thoroughly studied in recent years [9]. Some of these studies involve only face mask detection [10]; some of them include social distance measurement [11] and some with mixed results. Several works in this context of automatic coronavirus prevention have been discussed briefly in the subsequent paragraphs.…”

Untitled

“…The results of the study indicate that the framework supervises social distance between people efficiently. Ansari and Singh (2021) proposed a structure that tracks people for supervising social distancing which is being trained. To achieve this objective of social distancing supervision an algorithm is evolved using the object detection method.…”

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

“… ( Shareef et al, 2022 ) YOLOv4 A predefined SD threshold and a violation index are used to detect SD violations determine when the violation Mall-D, PETS2009, OTC, VIRAT Acc=96% Privacy preservation was not considered and can not distinguish between individuals from the same family and strangers. ( Ansari et al, 2021 ) CNN Real-time VSDM INRIA Acc=98.50% Pedestrians overlapping can bias detection performance (unique camera) and validation on a small image dataset. ( Saponara et al, 2021 ) YOLOv2 Real-time VSDM and body temperature detection from thermal videos.…”

“…Moreover, this approach only focuses on an indoor manufactory-setup distance measurement and does not provide any statistical assessment on the virus spread. In Ansari, Singh, et al (2021) , a VSDM using a compact CNN-based sequential model is proposed to first detect pedestrians in video frames collected using CCTV cameras. In doing so, a sliding window concept has been adopted as a region proposal when detecting pedestrians in each frame.…”