Estimating pedestrian walking speed at street crossings using the YOLOv4 and deep SORT algorithms: Proof of principle.

There is evidence that existing standards for signal timing do not provide enough time for many pedestrians to safely cross intersections. Yet, current methods for studying this problem rely on inefficient manual observations. The objective of this work was to determine if the YOLOv4 and Deep SORT computer vision algorithms have the potential to be incorporated into automated measurement systems to measure and compare pedestrian walking speeds at one-stage and two-stage street crossings captured in birds-eye-view video. Walking speed was estimated for 1018 pedestrians at single-stage (591 pedestrians) and two-stage (427 pedestrians) street crossings. Pedestrians in the one-stage crossing were found to be significantly slower than pedestrians who crossed the two-stage crossing in one signal (1.19 ± 0.50 vs. 1.31 ± 0.49 m/s, p < 0.001). This proof of principle study demonstrated that the YOLOv4 and Deep SORT approaches are promising for estimating pedestrian walking speed.