COVID vision: An integrated face mask detector and social distancing tracker

Prasad, Janvi; Jain, Arushi; Velho, David B.A. De V.; Kumar, Sendhil

doi:10.1016/j.ijcce.2022.05.001

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…for a query is the average of the precision values at all the recall levels where a relevant item was retrieved, see Eqs. [1,2,3,4] where 𝑘 is the number of queries, and 𝐴𝑃 𝑖 Is the average precision (𝐴𝑃) for a given query (𝑖), T.P. is True Positive, T.N.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

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An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19

Elhanashi

Saponara²,

Zheng

2023

Real-Time Processing of Image, Depth and Video Information 2023

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COVID-19 is an infectious virus caused by acute respiratory syndrome SARS-CoV-2. It was first discovered in December 2019 in Wuhan, China. This ongoing pandemic caused infected cases, including many deaths around the world. Coronavirus is spread mainly by air droplets near the infected person due to sneezing, coughing, and talking. Pretrained DL models utilize large CNN layers, which require more disk size on IoTembedded devices and affect real-time detection. This research presents an integrated lightweight DL approach for real-time and multi-task (social distancing, mask detection, and facial temperature) video measurement to control the spread of coronavirus among individuals. The three tasks have used the most recent YOLO detectors (YOLOv7-tiny). It is an object detection model optimized based on the original YOLOv7 to simply the neural network architecture. The trained models have been evaluated in terms of mean average precision, Recall, and Precision to assess the algorithm performance. The proposed approach has been deployed and executed on NVIDIA devices (Jetson nano, Jetson Xavier AGX) composed of visible and thermal cameras. The visible camera is used for face mask detection, while the thermal camera is used for facial temperature measurement and social distancing. This research enriched the prevention system of COVID-19 by the integrated approach compared to the state-of-the-art methodologies. In addition, we obtained promising results for real-time detection. The proposed approach is suitable for a surveillance system to monitor social distancing, Face mask detection, and measuring the facial temperature among individuals.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…OVID-19 pandemic has brought about significant changes in how society functions, emphasizing maintaining social distancing and wearing masks to slow the spread of the virus [1][2][3]. To ensure that these protocols are being followed, there is a growing need for automated systems that can detect and measure compliance.…”

Section: Introductionmentioning

confidence: 99%

An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19

Elhanashi

Saponara²,

Zheng

2023

Real-Time Processing of Image, Depth and Video Information 2023

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show abstract

“…It was determined that an automatic method is necessary to detect people who don't wear the mask in real-time. Our facial recognition system can scan people's faces to see if a mask is present and if it is being worn properly [2]. Image analysis and computer vision present a significant challenge in the detection and recognition of faces [3].…”

Section: Introductionmentioning

confidence: 99%

Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19

Zeebaree,

Kareem

2023

ijmscs

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The global epidemic causes extensive damage. Several sectors, including tourism and recreation, have been halted. Since these projects require work together in close contact, it increases the danger of infection. To reduce the spread of COVID-19, keep a safe distance from others and always wear protective clothing. In light of this, we have developed COVID Vision, a system that uses Haar cascades as a classifier for a face mask detector. This will reduce reliance on employees while maintaining COVID-19 criteria. COVID Vision can determine if someone is wearing a mask or covering their lips live. According to the findings, the suggested system correctly displays the output result of human face detection within a range of 0.6 to 1.35 metres away, in lighting conditions ranging from medium to normal, and with facial angles falling within a range of ±40°. Face accessories are allowed on targeted photos as long as they don't cover the face. In conclusion, this approach may reduce the cost of real-time identity verification labour.

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“…In detecting mask wearers on campus, computer vision is used by taking pictures of students focused on the framed part of the face to check for mask use. From the image data taken, it is matched with the data set that has been given [5].…”

Section: Introductionmentioning

confidence: 99%

“…YOLO has high speed, accuracy and detectability for real-time processing. YOLO will detect faces (masked or not) and then use a Convolutional Neural Network (CNN) algorithm to classify them into two categories [5]. There are many studies related to the use of the YOLO model for several different cases such as those conducted by the following researchers [7]- [14] II.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Face Mask Detection Using Phyton Programming Language

Ceng Giap,

Erviana

2023

bit-Tech

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Since the beginning of the pandemic in 2019, people in Indonesia have been required to wear masks. Although until now the pandemic has ended, the need to use masks is still very much needed such as to maintain health, avoid air pollution and others. In detecting mask users, an application is needed that can help human work. Currently, the Python programming language is widely used to build applications in the field of computer vision, one of which is this face mask detection application. This application will detect mask users, whether they are wearing a mask or not. This developed application uses the Yolo model using the Face Mask Detection dataset developed by Larxel, where the Yolo model can work on the dataset provided. The test results show that the Yolo model can recognize mask users with an accuracy value above 90%. The second experiment was carried out to detect several faces of mask users, the Yolo model can recognize mask users or not with an average accuracy value of 91.75%. For future research, it is also expected to use other models besides Yolo and make comparisons of several models and make improvements to the problems that exist in each model and use real time data.

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COVID vision: An integrated face mask detector and social distancing tracker

Cited by 6 publications

References 13 publications

An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19

An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19

Face Mask Detection Using Haar Cascades Classifier To Reduce The Risk Of Coved-19

Implementation of Face Mask Detection Using Phyton Programming Language

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