Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

Zuo, Fan; Gao, Jingqin; Kurkcu, Abdullah; Yang, Hong; Özbay, Kaan; Ma, Qingyu

doi:10.1016/j.jth.2021.101032

Cited by 23 publications

(19 citation statements)

References 38 publications

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“…On the one hand, object detectors, such as YOLO models [8], are more general-purpose than pose estimation techniques, but their utility for identifying human subjects may require pruning and/or retraining. In addition, they do not offer further information about the detected objects and their bounding boxes can be over-sized or incomplete [6]. On the other hand, pose estimators are specialized models; hence, they are more suitable to detect people in a scene.…”

Section: Person Detection and Localizationmentioning

confidence: 99%

“…The aforementioned solutions, although accurate, are not suitable, because they carry person-specific information which hinders their adoption for privacy-preserving applications. Nonetheless, privacy-preserving techniques are developed in [6,15] to monitor the evolution of social distancing patterns using CCTV cameras. The first work utilizes YOLO-v3 to detect pedestrians and the bounding box centroid for localization.…”

Section: Person Detection and Localizationmentioning

confidence: 99%

“…In general, the models' performance is conjoined with privacy; they yield high performance by carrying and processing person-specific information to develop robustness against occlusions and missing data [4]. In addition, they localize human subjects via bounding boxes that can be over-sized or incomplete which results in significant distance estimation errors [6]. Therefore, we propose a privacy-preserving adaptive social distance estimation and crowd monitoring system that can be implemented on top of any existing CCTV infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Al-Sa'D

Kıranyaz

Ahmad

et al. 2022

Sensors

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Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

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Section: Person Detection and Localizationmentioning

confidence: 99%

Section: Person Detection and Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Al-Sa'D

Kıranyaz

Ahmad

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…The Netherlands, where our study takes place. The few studies that have also studied social distancing behavior using video data (Pouw, Toschi, van Schadewijk et al 2020, Rahim, Maqbool and Rana 2021, Zuo, Gao, Kurkcu et al 2021) focus primarily on the required computer science techniques, and do not speak to a social science audience. The aim of the present study was to measure and monitor, over the course of the pandemic, the level of compliance to social distancing directives in urban public space.…”

Section: Research Problemmentioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco¹,

Hoeben²,

Koelma³

et al. 2021

Preprint

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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and run the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical limitations.

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“…In contrast, Noh et al (2020) used the Mask R-CNN ( He et al 2018 ) algorithm to detect pedestrian crossing behaviors with 400 days of closed-circuit television videos. Similarly, Zuo et al (2021) applied Yolo ( Redmon and Farhadi 2018 ) to videos collected from 731 locations in New York City to identify pairs of pedestrians that violate the social distancing regulation.…”

Section: Advances In Studying Pedestrian Experience Through Smart Datamentioning

confidence: 99%

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science

Fan

Loo

2021

Comput.Urban Sci.

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Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

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Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

Cited by 23 publications

References 38 publications

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science

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