e-space
Manchester Metropolitan University's Research Repository

    A novel social distancing analysis in urban public space: a new online spatio-temporal trajectory approach

    Su, Jie, He, Xiaohai, Qing, Linbo, Niu, Tong, Cheng, Yongqiang and Peng, Yonghong ORCID logoORCID: https://orcid.org/0000-0002-5508-1819 (2021) A novel social distancing analysis in urban public space: a new online spatio-temporal trajectory approach. Sustainable Cities and Society, 68. 102765. ISSN 2210-6707

    [img]
    Preview
    Accepted Version
    Available under License Creative Commons Attribution Non-commercial No Derivatives.

    Download (2MB) | Preview

    Abstract

    Social distancing in public spaces plays a crucial role in controlling or slowing down the spread of coronavirus during the COVID-19 pandemic. Visual Social Distancing (VSD) offers an opportunity for real-time measuring and analysing the physical distance between pedestrians using surveillance videos in public spaces. It potentially provides new evidence for implementing effective prevention measures of the pandemic. The existing VSD methods developed in the literature are primarily based on frame-by-frame pedestrian detection, addressing the VSD problem from a static and local perspective. In this paper, we propose a new online multi-pedestrian tracking approach for spatio-temporal trajectory and its application to multi-scale social distancing measuring and analysis. Firstly, an online multi-pedestrian tracking method is proposed to obtain the trajectories of pedestrians in public spaces, based on hierarchical data association. Then, a new VSD method based on spatio-temporal trajectories is proposed. The proposed method not only considers the Euclidean distance between tracking objects frame-by-frame but also takes into account the discrete Fréchet distance between trajectories, hence forms a comprehensive solution from both static and dynamic, local and holistic perspectives. We evaluated the performance of the proposed tracking method using the public dataset MOT16 benchmark. We also collected our own pedestrian dataset “SCU-VSD” and designed a multi-scale VSD analysis scheme for benchmarking the performance of the social distancing monitoring in the crowd. Experiments have demonstrated that the proposed method achieved outstanding performance on the analysis of social distancing.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    64Downloads
    6 month trend
    29Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record