Paper Title : Live Social Distance Detection Using Deep Learning Model

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6410066

Authors: Dr. E.K. Vellingiriraj ME., Ph.D., G.Sanmuga Priya

         



MLA Style: Live Social Distance Detection Using Deep Learning Model " Dr. E.K. Vellingiriraj ME., Ph.D., G.Sanmuga Priya " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Live Social Distance Detection Using Deep Learning Model " Dr. E.K. Vellingiriraj ME., Ph.D., G.Sanmuga Priya " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
To prevent the spreading of COVID, the only way is social distancing. Nowadays, AI teams create social distancing tools using the computer vision concepts. This project proposed a methodology to find social distance with the help of deep learning to evaluation the distance between people for mitigating the impact of corona virus pandemic. The detection tool was developed to notify people to keep safe safety distance among each other through evaluation of a video input feed. The video frame from ‘mp4’ file was given as input, and object detection pre-trained model based on YOLOv3 algorithm was applied for pedestrian detection. Then, video frame was converted into top-down view to measure distance from 2D plane. The distance among people was estimated and any noncompliant pair of people in display is indicated with red frame and red line. The proposed method was validated on pre-recorded video for pedestrians walking on street. The output result verified that proposed method is able to determine social distancing measures between people group in the video. The developed technique may be further developed as detection tool in real time application. The project is designed using Python 3.5 with opencv python 4.2.

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Keywords
— Social Distance Monitoring, Covid 19, Human Object Detection.