ABSTRACT
The covid-19 pandemic has affected both the health and lifestyle of the people, not only this the global economy also affected badly. The virus spreads at a very high rate and people can easily be infected. So for that people have to take a vaccine that is provided by their government but only vaccination is not a complete solution to this virus. It doesn't gives a full guarantee to prevent people's lives even after the vaccination, so we have to defend ourselves from the spread of the viruses as much as possible. For that mask and social distancing are the main key factor that is also recommended by the government and the public health agencies. As people are not habitual of wearing the mask so many times people forget to wear a mask in public places which is one of the main reasons for the spreading of the virus. Sometimes we see that at crowded places like metro stations and malls and universities, two or three guards are always present, to check the thermal temperature and if people are wearing masks or not and telling people to maintain a social distance. So there are a lot of problems in this because the metro station is a crowded place there people have to make a queue for checking their temperature so it is somehow hard to maintain social distancing and if there is an infected person found in the queue then all other surrounded people will also become infected. So we decided to solve this problem by contributing to the public health sector by making a complete system that will check if people are wearing a mask on their face properly or not. It will also check the thermal temperature of the people through the cameras and checks if someone is not violating social distancing rule. This will prevent people from infected people and also save the time of people. Now a maximum of one guard is needed for monitoring. To make this project practically we are taking the help of machine learning and deep learning. We will be using face detection and recognition algorithms that will be detecting the faces of people. We are using python as programming language. For face detection, we are using YOLO v4(You look only once) which supports a Convolutional neural network. So our process flow will be like that first we import all libraries and after that, we will build a neural network and after that training will be done on a model and then testing the model. After that system will become ready to deploy on the cloud. © 2022 IEEE.
ABSTRACT
Day by day, COVID-19 cases are increasing all over the world. Without a proper vaccine to control the disease, the only solution so far is social distancing and identifying the disease at an early stage. In more than 80% of confirmed cases there are only mild symptoms, like fever;therefore, we have to check the body temperature of people in public places like shopping malls, hotels, airports, schools and universities, etc. In this chapter we propose contactless temperature (CT) measurement utilizing thermal (TS), RGB, and 3D sensors. We also propose a fever location camera (FLC) which gives high-quality estimates from up to 2 or 3 meters away. Using cutting-edge technology, the fever location framework (FLF) estimates the internal heat level of individuals in groups of three or four by checking and filtering their face temperatures. If a high temperature is identified, the framework sounds an alarm or cautioning message, which has propelled face recognition technology. The framework, which is based on the investigation of face temperature, guarantees high-quality estimations. Using facial recognition (FR) likewise limits false readings;for example, an individual carrying a hot beverage. Using a devoted programming stage, a signal can be set to inform us of unusual temperatures. It can precisely recognize the facial temperature (FT) of numerous individuals quickly, with an exactness of ≤ 0.3 °C. Temperature recognition range can be set with the ideal location of up to 3 meters in the framework highlighted by a bi-directional double-channel (infrared light + visible light) camera utilizing a heated sensor and low level interference signals. © 2021 Scrivener Publishing LLC.
ABSTRACT
The world population is growing day by day but the resources are limited. So, on one hand, we have to fulfill our requirements by using resources in an optimized manner, and on the other hand, we are currently in a pandemic for which social distancing is the key to prevent ourselves from the COVID-19 virus. We all travel by bus, train, metros, and other public transports, and we all have faced the problem of overcrowding in public transport once in our life especially during any festive season when there is a lot of rush among people to go from their workplace to their hometown. The problem of overcrowding is very common in developing countries like India where the population is relatively higher than the number of seats in public transports. For instance, if we look at the Delhi metro in which it is almost impossible to travel during the festive season. However, it’s a regular problem, but the crowd becomes more when any festive season approaches. And due to such rush, people tend to just get inside the metro without even searching for a relatively less crowded or vacant seat/compartment, resulting in an unusual pattern of density seen across the metro, somewhere empty and somewhere highly crowded that even no place to stand. And now due to the COVID-19 pandemic, the situation worsens, on one hand, we are bound to travel in-crowd for work and on the other hand, we have to maintain social distancing for COVID prevention. So it becomes almost impossible to travel through such public transport. To solve such problems we are proposing a solution, that is, a system that takes care of the public places/transports by scanning and informing passengers where they can move to find a seat or less crowded. It also helps to ensure social distancing in the times of the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Summary Day by day, COVID-19 cases are increasing all over the world. Without a proper vaccine to control the disease, the only solution so far is social distancing and identifying the disease at an early stage. In more than 80% of confirmed cases there are only mild symptoms, like fever;therefore, we have to check the body temperature of people in public places like shopping malls, hotels, airports, schools and universities, etc. In this chapter we propose contactless temperature (CT) measurement utilizing thermal (TS), RGB, and 3D sensors. We also propose a fever location camera (FLC) which gives high-quality estimates from up to 2 or 3 meters away. Using cutting-edge technology, the fever location framework (FLF) estimates the internal heat level of individuals in groups of three or four by checking and filtering their face temperatures. If a high temperature is identified, the framework sounds an alarm or cautioning message, which has propelled face recognition technology. The framework, which is based on the investigation of face temperature, guarantees high-quality estimations. Using facial recognition (FR) likewise limits false readings;for example, an individual carrying a hot beverage. Using a devoted programming stage, a signal can be set to inform us of unusual temperatures. It can precisely recognize the facial temperature (FT) of numerous individuals quickly, with an exactness of ≤ 0.3 °C. Temperature recognition range can be set with the ideal location of up to 3 meters in the framework highlighted by a bi-directional double-channel (infrared light + visible light) camera utilizing a heated sensor and low level interference signals. The production of biomolecules that require human-specific lipid environments is extremely useful for basic research and medical applications. In article number 2000154, Seong-Jun Kim, Jae-Sung Woo, Sangsu Bae, and co-workers integrate multiple proteins or virus antigens into defined transcriptional hotspots in the human genome via a homology-independent targeted insertion method using CRISPR nucleases. This system is similar to a production pipeline of biomolecules in a factory controlled by CRISPR.