At the 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo’21), Columbia PhD student Mahshid Ghasemi will present the paper Auto-SDA: Automated Video-based Social Distancing Analyzer [1].

 To assess pedestrians’ compliance with social distancing policies,  the pilot site of the  COSMOS testbed was used in order to design and evaluate an Automated video-based Social Distancing Analyzer (Auto-SDA) pipeline. Auto-SDA derives pedestrians’ trajectories and measures the duration of close proximity events. It relies on an object detector and a tracker, however, to achieve highly accurate social distancing analysis, 3 modules were designed and incorporated into Auto-SDA: (i) a calibration module that converts 2D pixel distances to 3D on-ground distances with less than 10 cm error, (ii) a correction module that identifies pedestrians who were missed or assigned duplicate IDs by the object detection- tracker and rectifies their IDs, and (iii) a group detection module that identifies affiliated pedestrians (i.e., pedestrians who walk together as a social group) and excludes them from the social distancing violation analysis. Auto-SDA was applied to videos recorded at the COSMOS pilot site before the pandemic, soon after the lockdown, and after the vaccines became broadly available. Impacts of the social distancing protocols on pedestrians’ behaviors and their evolution were analyzed. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians violated the social distancing protocols, whereas this percentage increased to 65% after the vaccines became available. Moreover, after the lockdown, 0-20% of the pedestrians were affiliated with a social group, compared to 10-45% once the vaccines became available. Finally, following the lockdown, the density of the pedestrians at the intersection decreased by almost 50%.