Estimating pedestrian walking speed at street crossings using the YOLOv4 and deep SORT algorithms: Proof of principle
本研究验证了YOLOv4和Deep SORT计算机视觉算法能否用于自动测量行人过街速度，发现单阶段过街的行人比两阶段过街的行人速度更慢，为改进信号配时提供了新方法。
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.