使用YOLOv8和跟踪算法进行停车时间违规追踪.
Nabin Sharma1, Sushish Baral1, May Phu Paing2
1Department of Robotics and AI, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究介绍了一种低成本的停车时间违规跟踪系统,使用CCTV和AI. 该系统有效监控车辆,提高执法效率并降低与手动监控相关的成本.
科学领域:
- 计算机视觉和人工智能的人工智能
- 交通管理系统 交通管理系统
- 监控技术 监控技术 监控技术
背景情况:
- 违反停车时间是泰国的一个重大问题,目前由CCTV和人类监控管理.
- 现有的方法资源密集,在检测违规行为时可能缺乏一致的效率.
- 需要一个具有成本效益和自动化解决方案来监控停车时间限制.
研究的目的:
- 开发和评估一个低成本的停车时间违规追踪系统.
- 利用CCTV,深度学习和对象跟踪来自动检测违规行为.
- 在本应用程序中评估最先进的检测和跟踪算法 (SOTA) 的性能.
主要方法:
- 使用闭路电视 (CCTV) 进行视频监控.
- 采用YOLOv8用于对象检测和DeepSORT/OC-SORT算法用于对象跟踪.
- 实施时间限制条件,以识别和记录违反停车时间的情况.
主要成果:
- 在跟踪算法中实现了高性能,多对象跟踪精度 (MOTA) 在各种数据集中达到DeepSORT的1.0和OC-SORT的0.90.
- 证明了系统能够准确跟踪车辆并执行时间限制的能力.
- 与传统方法相比,综合系统的性能有所改善.
结论:
- 拟议的系统提供了一种可行的,低成本的解决方案,用于自动检测违规停车时间.
- 深度学习和对象跟踪算法显著提高了监控系统的效率和准确性.
- 这种方法为交通管理和执法提供了一种新且有效的方法.
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