盘子:一个感知延迟意识的估计器
Rodrigo Aldana-López1, Rosario Aragüés1, Carlos Sagüés1
1Departamento de Informatica e Ingenieria de Sistemas (DIIS) and Instituto de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, Zaragoza 50018, Spain.
ISA transactions
|August 25, 2023
概括
本研究引入了用于目标追踪的感知-延迟意识估计器 (PLATE). PLATE通过平衡感知延迟和检测准确性来优化性能,改善动态环境中的资源使用.
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
- 机器学习 机器学习
背景情况:
- 目标跟踪对于许多应用程序至关重要,精度通常随着计算力度 (感知延迟) 的增加而增加.
- 高感知延迟并不总是最佳的,特别是当计算资源共享或环境要求更低的延迟时.
研究的目的:
- 开发一种新的感知-LATency意识估计器 (PLATE) 来优化目标跟踪性能.
- 在感知延迟,检测精度和计算资源利用之间实现平衡.
主要方法:
- 拟议的PLATE系统可以随着时间的推移动态调整感知配置.
- 它优化了性能测量,考虑了延迟和准确性权衡.
- 提供了正式的复杂性和最佳性分析,将其与启发式跳过框架的方法区分开来.
主要成果:
- 实验证明了PLATE在优化知觉延迟和精度方面的有效性.
- 评估是根据使用现实数据的标准基准进行的.
- 在感知阶段使用了最先进的深度学习对象检测方法.
结论:
- PLATE提供了一种有效的方法来管理感知延迟和目标跟踪准确性之间的权衡.
- 该方法提供了一种原则性的方法来优化动态计算环境中的资源使用.
- 实验验证证证实了PLATE对现有技术的优势.
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