International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
Real Time Social Distance Detector using Deep learning MARADANI SUDHATRI1, KILARI VENKATA SAI DHAREESH2, IRALA KRANTHIKUMAR3 1Dept.
Of ECE, LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING, Mylavaram, Andhra Pradesh 2Dept. of CSE, Presidency University, Bangalore, India 3Dept. Of ECE, SRM Institute of Science and Technology, Kattankulathur, Chennai, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The COVID-19 epidemic has unquestionably stopped all human activity. The world we were living in a few months ago is quite different from the one we are living in now. The virus is dangerous to humanity and is rapidly spreading. Given the urgent need, one must constantly take some measures, one of which is social estrangement. To guarantee a decrease in the growing rate of new cases during COVID-19, maintaining social distance is essential. The major goal of our text is to determine whether others around us are keeping social distance. The SocialdistancingNet-19 model we created for identifying a person's frame and presenting labels marks them as safe or dangerous depending on whether the distance is more than a certain threshold. People may be watched over with this technique and CCTV video surveillance. Our model has a 92.8 percent accuracy rate. Key Words: Covid19, Social Distance, Object detection. 1. INTRODUCTION The corona virus-2 severe acute respiratory syndrome is the viral illness that causes coronavirus. The illness was originally discovered in Wuhan, China, in December, which helped it spread over the globe. The virus mostly transmits between people when they are in proximity, particularly via microscopic droplets produced while coughing or sneezing. Droplets that fall on the ground will go through a person's body and through the air. The illness is most contagious during the first three days. Several common symptoms include weariness, a dry cough, and nausea. A global pause has resulted from severe and catastrophic human consequences. Such symptoms often include headache and sore throat. A person with minor symptoms needs two weeks to recover. Individuals with severe symptoms may take longer to heal depending on the severity and immune system of the person. The World Health Organization (WHO) recommends using the phrase "social separation" considering the disease's destructive spread. Maintaining physical distance is crucial to reduce the disease's pace of transmission. To be secure and return to the world we lived in a few months ago, two metres must always be maintained between any two people. Following the COVID-19 pandemic, the CDC revised the definition of social distance as staying away from congregate areas, forbidding public gatherings, and maintaining, where necessary, a space of about six feet or two metres from everyone. Recent research has shown that during exercise,
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sneeze or deep inhale droplets may travel more than six metres. Thus, maintaining the social distance standard is both necessary and advantageous for us to live a safer and better life. Our research suggests a way to assess a person's compliance with the social distance norm. The results are validated via a video feed and a live webcast. We can determine if a person is maintaining social distance by gauging the distance between two frames of them from their centroids. They are marked as safe and harmful as well. 2. Literature Review Several studies, including a wide range of research approaches, have been conducted on the topic of social distance. To put a halt to the transmission of the Covid-19 virus, Yadav et al. [1] proposed a system that used a Raspberry Pi4 computer that was fitted with a camera to carry out automated, round-the-clock surveillance of public areas. A trained model and a bespoke data set were loaded onto a Raspberry Pi 4 before it was equipped with a camera and given its configuration. The model in the raspberry pi4 receives real-time footage of public spaces from the camera, and it continuously and automatically analyses those spaces to determine whether people are wearing masks and whether individuals maintain acceptable social distances between themselves and one another. Their system operates in two stages: first, when a person is recognized without a mask, a photo is taken and sent to a control center at the State Police Headquarters; and second, when an individual consistently violates the social distance rule within the threshold time, an alarm sounds, warning them to keep their distance from one another, and a critical alert is sent to the control center at the State Police Headquarters for further action. When a person is recognized without a mask, the photo is sent to a control center at the State They achieved an accuracy level of 91 percent. Singh Punn et al. [4] presented a real-time based deep learning method to assess social distance. This method makes use of object recognition and tracking methods. After counting the number of groups that were formed, we were able to determine the total number of violations, and the violation index term was computed by dividing the total number of persons by the total number of groupings. A variety of object detection models, such as Faster RCNN, SSD, and YOLO v3, were used. These models achieved a satisfactory balance of FPS and mAP score performance. Yang et al.proposed an AI monocular camerabased real-time system to measure social distance [5].
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