Social Distance Capturing and Alerting Tool

Yeshasvi, Mogula; Bind, Veeramachaneni; Subetha, T.

doi:10.1109/icspc51351.2021.9451695

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…On the other hand, several social distance monitoring systems based on fixed or mobile digital cameras have been developed. Yeshasvi et al [27] designed an effective social distancing estimation and alerting system that utilizes surveillance video as input to estimate humans' social distance and urge person to maintain social distance. Ahmed et al [28] leveraged the object recognition method based on YOLO v3 to recognize pedestrians and estimate their mutual distance.…”

Section: Social Distance Estimationmentioning

confidence: 99%

A Smartphone-based Zero-Effort Method for Mitigating Epidemic Propagation

Wang

et al. 2023

Preprint

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A large number of epidemics, including COVID-19 and SARS, spread through droplets, quickly swept the world and claimed the precious lives of large numbers of people. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. The hierarchical attention mechanism is used to find local important information through global scanning, then enhances useful information and suppresses useless information. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

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Section: Social Distance Estimationmentioning

confidence: 99%

A Smartphone-based Zero-Effort Method for Mitigating Epidemic Propagation

Wang

et al. 2023

Preprint

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show abstract

“…On the other hand, several social distance monitoring systems based on fixed or mobile digital cameras have been developed. Yeshasvi et al [24] designed an effective social distancing estimation and alerting system that utilizes surveillance video as input to estimate humans' social distance and urge person to maintain social distance. Ahmed et al [25] leveraged the object recognition method based on YOLO v3 to recognize pedestrians and estimate their mutual distance.…”

Section: Social Distance Estimationmentioning

confidence: 99%

A smartphone-based zero-effort method for mitigating epidemic propagation

Wang

et al. 2023

EURASIP J. Adv. Signal Process.

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A large number of epidemics, including COVID-19 and SARS, quickly swept the world and claimed the precious lives of large numbers of people. Due to the concealment and rapid spread of the virus, it is difficult to track down individuals with mild or asymptomatic symptoms with limited human resources. Building a low-cost and real-time epidemic early warning system to identify individuals who have been in contact with infected individuals and determine whether they need to be quarantined is an effective means to mitigate the spread of the epidemic. In this paper, we propose a smartphone-based zero-effort epidemic warning method for mitigating epidemic propagation. Firstly, we recognize epidemic-related voice activity relevant to epidemics spread by hierarchical attention mechanism and temporal convolutional network. Subsequently, we estimate the social distance between users through sensors built-in smartphone. Furthermore, we combine Wi-Fi network logs and social distance to comprehensively judge whether there is spatiotemporal contact between users and determine the duration of contact. Finally, we estimate infection risk based on epidemic-related vocal activity, social distance, and contact time. We conduct a large number of well-designed experiments in typical scenarios to fully verify the proposed method. The proposed method does not rely on any additional infrastructure and historical training data, which is conducive to integration with epidemic prevention and control systems and large-scale applications.

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“…In order to detect people/objects, some methods [21,15] rely on YOLO, other methods [18,10] use Faster R-CNN and still other methods [8] use GMM-based foreground detection. However, this is not the focus of this paper; we assume that bounding boxes around people are available.…”

Section: Related Workmentioning

confidence: 99%

“…However, the narrower problem of estimating the distance between people has gained attention only recently. In particular, the COVID pandemic has sparked interest in inconspicuous monitoring of social-distance violations (e.g., less than 6ft) [8,10,18,21,11,1,16]. A natural, cost-effective resource that can be leveraged to accomplish this goal are the surveillance cameras widely deployed in commercial, office and academic buildings.…”

Section: Introductionmentioning

confidence: 99%

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

Cokbas

Ishwar

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.

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Social Distance Capturing and Alerting Tool

Cited by 4 publications

References 6 publications

A Smartphone-based Zero-Effort Method for Mitigating Epidemic Propagation

A Smartphone-based Zero-Effort Method for Mitigating Epidemic Propagation

A smartphone-based zero-effort method for mitigating epidemic propagation

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

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