Single Image Human Proxemics Estimation for Visual Social Distancing

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

“…Many variants of the YOLO model and other neural network architectures are used to detect humans in videos and the bounding box centroid, top left edge, or bottom center, is used for localization [16][17][18][19][20][21][22][23][24][25]. Lastly, the social distancing problem is tackled in [26] using a pose estimation model to detect human subjects in videos and to infer their location using the predicted feet joints. The same approach is employed in [27] to measure inter-personal distances but for still images.…”

“…Moreover, inter-personal distances are computed in [6] and the violations are identified at three safety levels; 1, 1.8, and 3.6 m. The study concludes that incomplete or over-sized bounding boxes introduce significant errors to the distance calculation; hence, selecting an appropriate person detector is paramount to the system's feasibility and success. Finally, in [26], pair-wise distances are approximated through the estimated body joints and social distance infractions are identified by a 2 m threshold.…”

“…The former describes the spatial signature exerted by the subjects in the surveilled scene, while the latter encodes the spatial impression of social distance infractions [35]. Similarly, heatmaps are generated in [13,26,36] to represent the regions in which social distance violations are frequent. These studies demonstrate that short and long-term occupancy/crowd density maps are important to identify high-risk regions in the scene.…”

“…In [26], given the non-empty set of feet joints j 12 m , j 15 m , j 20 m , j 21 m , j 22 m , j 23 m , j 24 m , j 25 m and the condition #J m ≥ 13, the ground position of subject m is estimated as follows:…”

“…Moreover, for the vertical coordinate v m , we relax the requirement for detecting the feet joints by exploiting the human average skeletal characteristics. More specifically, we use the ratio between the torso and lower body lengths to infer the ground position vertical coordinate [26], i.e., (0.85/0.6) in line 15. Finally, regardless of the approach, we discard any estimated positions outside the user-defined ROI-see Figure 2.…”