Social Distancing Detector using Deep Learning
Manthri Sriharsha1, Lalith Sai Allani2, Akhila Gandla3, Sowjanya Jindam4
1Manthri Sriharsha*, Department of Information Technology, MVSR Engineering College, Osmania University, Hyderabad (Telangana), India.
2Sowjanya Jindam, Department of Information Technology, MVSR Engineering College, Osmania University, Hyderabad (Telangana), India.
3Akhila Gandla, Department of Information Technology, MVSR Engineering College, Osmania University, Hyderabad (Telangana), India.
4Lalith Sai Allani, Department of Information Technology, MVSR Engineering College, Osmania University, Hyderabad (Telangana), India.
Manuscript received on December 20, 2021. | Revised Manuscript received on January 30, 2022. | Manuscript published on March 30, 2022. | PP: 146-149 | Volume-10 Issue-5, January 2022. | Retrieval Number: 100.1/ijrte.E67100110522 | DOI: 10.35940/ijrte.E6710.0110522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social Distancing is the best possible way to detain the spread of Covid-19. Even though vaccine has been found and working effectively in saving the lives of people, social distancing is necessary to reduce the spread of virus to maximum extent which not only saves people from being infected but also reduces the impact of spreading of the disease. In our proposed system, we use Deep Learning with python to monitor social distancing in public places. This is a software tool that monitor if people are maintaining proper social distancing norms or not by analyzing real time video streams from CC camera. We use YOLO Model which is trained by COCO dataset.
Keywords: Social Distancing, Deep Learning, YOLO, COCO. Abbreviations and Acronyms, YOLO-You Only Look Once, COCO-Common Objects in Context, DNN- Deep Neural Network
Scope of the Article: Deep Learning