Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 22 Issue 1

average risk is determined as well. This research will conduct an in-depth examination of the usage of masks to prevent the transmission of the lethal coronavirus. This version introduces a novel multi - facemask detection in real-time. Finally, the Human Tracking and Profiling system calculates the average risk associated with four components and then calculates the total average risks involved with those four components. II. L iterature R eview The main intention is to implement automated Human tracking and profiling for risk management application to avoid speed of spreading the virus infections in the world. Social Distance Risk, Face-mask risk, Density Risk and Human Actions and behaviors Risk are the main four components to minimize the percentage of deaths due to viruses. Lot of applications developed for analysis the risk and get the crowd/ visitors count in the frame but there is not single system to analysis the density risk in a particular area to minimize the spreading viruses among people. In the current pandemic situation in world, one Deep-CNN Crowd Counting Model for enforcing social distancing application is implemented in Saudi Arabia’s public places for avoid spreading the viruses among peoples [1]. Actually, above proposed method is based on CNN model to count people who appear in video frames in public places [1]. Another, people counting system developed in post COVID-19,which is counting people through infrared detection and this system count and update based on people moving in/out through the area/premise [2]. There already exists a few work that pedestrian counting systems [[3], [4]]. so, in this proposed density risk solution go beyond above systems and in the first step, a video first frame user [system owner] must selects the area where he/she wants to measure the density risk using four mouse click points. Then according to area width and length system estimate the maximum people/visitors count allowed in that area. After that, system take the real time total people/visitors count inside the user selected area (where user wants to measure the density risk) in each video frames and system comparing the these both real time people count, and maximum people count allowed in this area and analysis get density risk. If the real people count is higher than the maximum people count allowed in the area, then that area is a high-risk place. This is the novelty of density risk analysis. When it comes to the face-mask risk, the majority of the research studies reviewed focused exclusively on identifying the face mask. Researchers used a variety of machine learning and deep learning algorithms to assess whether or not they were wearing a face mask. Using image processing, the device developed by a team led by S. Balaji detected the passengers’ facemasks [9]. Additionally, the team, which includes Amit Chavda, has presented a method that uses a Convolutional Neural Network to detect individuals who use facemasks [7] and some of research papers used to detect facemask by utilizing Faster-RCNN [[6], [8], [10]]. Numerous devices have been proposed and implemented to detect facemasks using various methodologies, however analysers all have significant limitations. Numerous facemask types have been introduced to the market. Even if individuals are masked, it is impossible to demonstrate that they are passing hatred from one individual to another. This is because they must cover their nose and mouth and secure it beneath their chin, even if they are wearing a mask. Additionally, it clings snugly to their chin. The National Center for Immunization and Respiratory Diseases (NCIRD) has confirmed that the viral transmission rate varies between different types of masks. Certain masks are designed and tested to ensure consistent performance in preventing the transmission of COVID-19. These masks are labeled with the criteria they comply with. KN95 masks provide approximately 98.5% protection, whereas surgical masks provide 56.1 percent protection. Some folks make their homemade masks. This results in a 51.4% guard. We concentrated on that and identified the facemask as surgical, KN95, and homemade using the YOLO principle (V3). Thus, our proposed system analyzes the multi-person real-time face mask type and analyzes the risk of face masks and unmasking. According to the investigation, if it exceeds 75%, it is considered a risky zone. As soon as it becomes a risky area, the head of the location is warned through SMS. Another important aspect of human profiling and estimating for risk management systems is the estimation of human actions and behaviors. In the study publications, systems to detect an individual’s activity were introduced, but systems to recognize the action of a group of individuals were not found. Zhe Cao and colleagues focused on a critical component of acquiring a deep understanding of humans in photos and videos: human two-dimensional posture estimation—or the difficulty of localizing anatomical important points or ”parts.” Human estimating has always been primarily concerned with locating individuals’ body components [14]. Federico Angelini and his colleagues proposed Action Pose: a two-dimensional pose-based technique for human action recognition at the pose level [15]. They retrieved low-level and high-level features fo the Action XPose from the human body posture and fed them into a LSTM (Long Short-Term Memory Neural) Network and a 1D Convolutional Neural Network for classification. Action XPose, a 2D pose-based algorithm for posture-level Recogninizing Human Action, was introduced by Zeyu Fu and his team [16]. However, our suggested system recognizes the action of human and classifies actions such as leap, run, and walk. The risk is assessed based on the classification of human behavior © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 10 ( )D Human Tracking and Profiling for Risk Management Year 2022