Social Distancing Detection and Analysis through Computer Vision

Madane, S. Srinivasa Rao; Chitre, D. K.

doi:10.1109/i2ct51068.2021.9418195

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…At the same time, OTC includes one video sequence collected on a pedestrian, which has 4501 frames recorded using a unique camera at 25 fps. In Madane and Chitre (2021) , the INRIA person dataset ( Dalal & Triggs, 2005 ) is utilized for training the DL models, which contains training and testing data and their corresponding annotations. While for inference or testing, the OTC dataset is considered along with the performance evaluation of tracking and surveillance (PETS 2009) dataset, in which both contain numerous crowd activities.…”

Section: Datasetsmentioning

confidence: 99%

“…EfficientDet: in Madane and Chitre (2021) , three pre-trained object detectors, namely EfficientDet-DO, EfficientDet-D5, and DETR, having ResNet-50 as a backbone, are used to detect pedestrians in public areas. Moving on, the fine-tuned models have been evaluated on OTC ( Davis & Sharma, 2007 ) and PETS ( Ferryman & Shahrokni, 2009 ) people tracking datasets.…”

Section: Visual Social Distancing Monitoring (Vsdm)mentioning

confidence: 99%

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Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Himeur

Al‐Maadeed

Almadeed

et al. 2022

Sustainable Cities and Society

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Section: Datasetsmentioning

confidence: 99%

Section: Visual Social Distancing Monitoring (Vsdm)mentioning

confidence: 99%

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Himeur

Al‐Maadeed

Almadeed

et al. 2022

Sustainable Cities and Society

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“…Two models were employed in the suggested framework of Madane and Dnyanoba ( 29 ). The first is EfficientDet, which is created by combining an EfficientNet backbone and weighted BiFPN.…”

Section: Related Studiesmentioning

confidence: 99%

Deep learning techniques for detecting and recognizing face masks: A survey

Alturki

Alharbi

AlAnzi

et al. 2022

Front. Public Health

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The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

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Real-time Social Distancing Detection Approach Using YOLO and Unmanned Aerial Vehicles

Merizalde

Morillo

2022

Smart Technologies, Systems and Applications

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Social Distancing Detection and Analysis through Computer Vision

Cited by 11 publications

References 13 publications

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey

Deep learning techniques for detecting and recognizing face masks: A survey

Real-time Social Distancing Detection Approach Using YOLO and Unmanned Aerial Vehicles

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