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

Fig. 9: Final output of Facemask detection Fig. 10: Results of several performance key metrics based on the prediction of various training data-set e) Overall Risk Calculation The authors were unable to obtain individual outputs for this system after considering the above situations (Social distance Risk, Social Density Risk, Human Behavior Risk, and face mask Risk). Because different health guidelines infractions can occur in the same public space As a result, overall risk must be estimated utilizing social distance risk, social density risk, human behavior risk, and face mask risk. The authors provide a new formula to calculate the overall risk using each functionality. Fig. 11: Overall Risk Calculation Formula Total risk categories must be determined in order to compute the overall risk. There are four risk categories, according to the system (Social distance Risk, Social Density Risk, Human Behavior Risk and face mask Risk). The total number of risk categories that have been breached should next be determined. Finally, using these variables, compute the overall risk. If the aggregate danger exceeds 75%, the area is considered high risk. If the entire risk is between 25% and 75%, the area is considered low risk. Finally, if the threat is less than 25%, the location is considered safe. V. C onclusion The use of machine learning becomes more common. By using the image processing and deep- learning techniques, i.e. YOLO, SSD, Open Pose, and Deep-Sort methods, we provide a comprehensive real- time person recognition system. Mainly covered four main scenarios. Those are density detection and analyze the risk, social distance detection and analysis of the risk, face-mask detection and analyze the risk, and human pose detection and analyze the risk. Test average precision (mAP) for detect humans and detect facemask with facemask type respectively 85.0 %, 73.0 %. To detect human behavior the system got 95.0% present of test accuracy. Our social distancing risk detection and estimating area length and width for density risk detection did not use correct camera calibration, which means that pixel distances to measurable real units were not (easily) mapped to (i.e., meters, feet, etc.). Therefore, the first step to improving our social distancing risk detection and estimating area length and width for density risk from the distance between our social systems is therefore to use a good camera calibration. That way, the results will be better and can calculate measurable units actually (rather than pixels).This work can be used as the basis for estimating the risk of each function. In the end, we are come up with the four individual average risks. Based on that we are calculating the total risk for a particular place. R eferences R éférences R eferencias 1. Salma Kammoun Jarraya, Maha Hamdan Alotibi, and Manar Salamah Ali1,”A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places”, 2020. 2. A.S.F Rahman1, S.B Yaakob1, A.R.A Razak1 and R.A Ramlee2,”Post COVID-19 implementation of a bidirectional counter with reduced complexity for people counting application”, 2021. 3. Melbourne—Pedestrian Counting System. Available online :https://www.melbourne.vic.gov.au/about-mel bourne/research-and-statistics/city-population/Page s/pedestrian-counting-system.aspx (accessed on 3 March 2021). 4. Fowzia Akhter, Sam Khadivizand, Hasin Reza Siddiquei, Md Eshrat E. Alahi and Subhas Mukhopa dhyay, ”IoT Enabled Intelligent Sensor Node for Smart City: Pedestrian Counting and Ambient Moni- toring”, 2019. 5. Nurul Iman Hassan, Fadhlan Hafizhelmi Kamaru Zaman, Nooritawati Md. Tahir, Habibah Hashim,” People Detection System Using YOLOv3 Algorithm ”, 2020. 6. Huaizu Jiang, Erik Learned-Miller,”Face Detection with the Faster R-CNN”, IEEE 12th International Conference, 2017. 7. Zhang Jialin1, Liu Yaofeng1, Jiang Zhendong1 and Zhang Heng1,”Research on medical mask detection method based on fast Fourier transform and linear Gauss”, IEEE/ASME International Conference on Advanced Intelligent Mechatronics Hong Kong, China, July 8-12, 2019. 8. Susanto, Febri Alwan Putra, Riska Analia, Ika Kar lina Laila Nur, ”The Face Mask Detection For Preventing the Spread of COVID-19 at Politeknik Negeri Batam”, IEEE International Conference, 2021. © 2022 Global Journals Global Journal of Computer Science and Technology Volume XXII Issue I Version I 14 ( )D Human Tracking and Profiling for Risk Management Year 2022