Identification of Face Mask and Social Distancing using YOLO Algorithm based on Machine Learning Approach

Kolpe, Rupali; Ghogare, Shubham; Jawale, M. A.; William, P.; Pawar, A. B.

doi:10.1109/iciccs53718.2022.9788241

Cited by 33 publications

(10 citation statements)

References 20 publications

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“…Using nose Keypoint positions to determine passenger location in the depth scene, proved as a suitable technique for measuring the distances between passengers and determining occupied seating positions. Other computer vision techniques for measuring social distancing rely upon bounding boxes for person detection [ 10 , 11 , 13 ], and inverse projection mapping/level floor surfaces for spatial reconstruction [ 10 , 11 , 12 , 14 ]. As opposed to these, the employed method in the presented research excels in that multiple positions of a person can be identified without any prior spatial knowledge or required surface conditions in the object space—allowing for immediate and detailed social distancing analysis to be performed.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, this has been achieved with the addition of pose estimation and pedestrian tracking (with low-resolution footage), for improved measurement ability [ 12 ]. With knowledge of extrinsic camera parameters such as height, and internal parameters such as focal length, a person’s distance to the camera device can be determined and converted to real-world distances before person-to-person distances are determined [ 13 ]. Pose estimation can also be utilised to enhance scene restoration with this approach, allowing for auto-calibration techniques before social distance measurement [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

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Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation

Bell,

James,

López-García

2023

Sensors

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In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social distancing ability on public transport as well as the use of advanced computer vision techniques to accurately measure this are explored in this paper. A low-cost depth-sensing system is deployed on a public bus as a means to approximate social distancing measures and study passenger habits in relation to social distancing. The results indicate that social distancing on this form of public transport is unlikely for an individual beyond a 28% occupancy threshold, with an 89% chance of being within 1–2 m from at least one other passenger and a 57% chance of being within less than one metre from another passenger at any one point in time. Passenger preference for seating is also analysed, which clearly demonstrates that for typical passengers, ease of access and comfort, as well as seats having a view, are preferred over maximising social-distancing measures. With a highly detailed and comprehensive set of acquired data and accurate measurement capability, the employed equipment and processing methodology also prove to be a robust approach for the application.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation

Bell,

James,

López-García

2023

Sensors

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“…Gallo et al [ 30 ] applied YOLO v7 in weeds and crop detection and achieved better performance than the other YOLO versions. Kolpe et al [ 31 ] used YOLO algorithm to identify masks and social distancing, eliminating the need for manual monitoring systems. Zhao et al [ 32 ] used the advanced YOLO v4 algorithm to identify unsafe shipborne mooring and unmooring operation behaviors.…”

Section: Related Workmentioning

confidence: 99%

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

Xue

et al. 2023

Sensors

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The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management.

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“…COVID-19 applications were also highly discussed in the literature review since it covers the year 2020 to 2022, the years where COVID-19 was still prominent in the society. Among the COVID-19 Applications, majority of the studies focused on the main protocols that is concerned with COVID-19: which is to ensure that Face Mask is worn and Social Distancing is being met at all times [1,23,24,26,28]. These studies implemented a Deep Learning Algorithm to implement its intended purpose and used an IP camera to capture the faces and the whole body of people before allowing the model to implement its algorithm.…”

Section: Various Applications Of Ip Cameramentioning

confidence: 99%

IP Cameras Beyond Security: A Systematic Literature Review Examining Their Adoption and Potential in the Healthcare Industry

Ong,

Tomas,

Tomas

2023

ijmst

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There is a growing interest of IP Camera usage in the healthcare industries. IP Cameras are now being used in healthcare including patient monitoring, healthcare, telemedicine, etc. This paper aims to determine the applications of the IP Camera in the healthcare industry including how it is used in these applications to serve as a tool for future researchers to understand how they can use IP cameras in their future endeavors. A total of 31 related literatures and studies published from 2019 to 2022 were gathered, all of which used IP cameras in their specific studies. A code for each study was created according to their specific application and use. Results show that IP cameras were mainly used in 12 applications, mostly for Deep Learning, Robotics, and COVID-19 Application. These cameras were also used in 7 roles, mainly remote monitoring, telemedicine, telerehabilitation, and capturing data for model training, testing, and use. Overall, this study showed that IP cameras play a crucial role in the healthcare industry and have the potential to improve and revolutionize the way healthcare services are delivered such as improving telemedicine and promoting the use of telerehabilitation. Future research includes examining the use of IP cameras in other industries, especially in smart home environments, how IP cameras are used in different deep learning algorithms, and analyzing the benefits and challenges of IP cameras in different applications. CCS CONCEPTS: CCS CONCEPTS -> General and reference ~ Document types ~ General literature ACM Reference Format: This block will be automatically generated when manuscripts are processed after acceptance.

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Identification of Face Mask and Social Distancing using YOLO Algorithm based on Machine Learning Approach

Cited by 33 publications

References 20 publications

Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation

Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

IP Cameras Beyond Security: A Systematic Literature Review Examining Their Adoption and Potential in the Healthcare Industry

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