使用YOLO和滑动创新过器进行对象检测和跟踪
Alexander Moksyakov1, Yuandi Wu2, Stephen Andrew Gadsden2
1College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究引入了一种使用You Only Look Once (YOLO) 和滑动创新过器的新物体检测和跟踪方法. 这种方法可以在带有干扰的动态环境中提高跟踪可靠性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 对象检测和跟踪在机器学习和计算机视觉中至关重要.
- 现有的方法与现实世界的挑战,如闭塞和干扰作斗争.
研究的目的:
- 提出一种新的物体检测和跟踪方法.
- 在动态和不确定的环境中应对挑战.
主要方法:
- 集成的你只看一次 (YOLO) 对象检测.
- 实施滑动创新过器,以实现强大的目标跟踪.
- 估计最佳心脏位置和轨迹更新.
主要成果:
- 拟议的基于过器的滑动创新跟踪优于传统的基于卡尔曼的方法.
- 在存在干扰的情况下,可以证明提高跟踪可靠性.
- 在监控场景中的实验模拟验证了该方法.
结论:
- 这项研究为对象检测和跟踪提供了实用和有效的解决方案.
- 滑动创新过器在扰乱和不确定的环境中证明了其稳健性.
- 这项工作为推进多对象跟踪应用提供了基础.
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