一个模糊的决策系统,用于在非静止条件下的多个对象的视频跟踪
Payam Safaei Fakhri1, Omid Asghari2, Sliva Sarspy3
1Department of Artificial Intelligence, Software Engineering, Islamic Azad University, Central Tehran Branch, Iran.
本研究介绍了一种计算机视觉方法,用于在实时监控和自主导航中跟踪多个移动物体. 该算法达到75%的准确性,并处理每秒43,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 在动态环境中跟踪多个移动对象是计算机视觉的一个持续挑战.
- 现有的方法在与非静止设置作斗争,影响了监控和自主导航等应用程序.
研究的目的:
- 开发和评估一种新的计算机视觉算法,用于在非静止环境中强大的多对象跟踪.
- 通过精确的实时对象监控来增强自主导航系统的能力.
主要方法:
- 使用Kanade-Lucas-Tomasi (KLT) 功能跟踪器来识别和跟踪连续之间的功能点.
- 使用运动信息和背景减去来隔离移动的物体.
- 应用模糊逻辑,使用质量中心和长宽比,用于对象分类和分离.
主要成果:
- 该算法成功地跟踪和分类车辆,行人,自行车和摩托车,准确度约为75%.
- 实现每秒43的处理速度,在现有方法上表现出优越的性能.
- 在相机的视野内,有效地实时监控移动的物体.
结论:
- 拟议的方法在用于自主导航和监控的多对象跟踪方面取得了重大进展.
- 该算法提供了高精度和高效处理速度的平衡,使其适合实时应用.
- 模糊逻辑集成使各种移动对象的有效分类成为可能.
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