使用YOLOv4和深度SORT算法在街道十字路口估计行人步行速度:原理证明
Hamed Ghomashchi1, Jakson Paterson2, Alison C Novak3
1KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Canada.
Applied ergonomics
|April 27, 2024
概括
像YOLOv4和Deep SORT这样的计算机视觉算法显示了自动测量行人步行速度的前景. 这项技术可以改善交通信号定时,使街道交叉路口更安全.
科学领域:
- 运输工程 运输工程
- 计算机视觉 计算机视觉
- 人类因素 人类因素
背景情况:
- 当前的行人安全标准可能不允许足够的时间跨过十字路口.
- 测量行人步行速度的现有方法,如手动观察,是低效的.
- 需要自动化系统来准确评估行人运动,并告知交通信号定时.
研究的目的:
- 评估YOLOv4和Deep SORT计算机视觉算法用于自动行人速度测量的潜力.
- 用视频分析对一阶段和两阶段街道交叉路口的行人步行速度进行比较.
主要方法:
- 使用了街道十字路口的鸟视图视频录像.
- 应用YOLOv4和Deep SORT算法来检测和跟踪行人.
- 在不同类型的过道上为1018名行人计算的步行速度.
主要成果:
- 使用计算机视觉算法,成功估计了行人步行速度.
- 两级十字路口的行人比一级十字路口的行人 (1.19 ± 0.50 m/s) 走得明显更快 (1.31 ± 0.49 m/s).
- 统计分析证实了两种交叉口类型之间的速度有显著差异 (p < 0.001).
结论:
- YOLOv4和Deep SORT算法展示了开发自动化系统测量行人步行速度的潜力.
- 这项技术为交通工程研究提供了一种更有效,更可扩展的替代手动观测方法.
- 研究结果表明,交叉路口设计 (一阶段与两阶段) 影响行人步行速度,影响信号定时需求.
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