资源意识的视频流 (RAViS) 框架用于使用深度学习算法进行对象检测系统
Ary Mazharuddin Shiddiqi1, Edo Dwi Yogatama1, Dini Adni Navastara1
1Department of Informatics, Institute Teknologi Sepuluh Nopember, Indonesia.
MethodsX
|August 3, 2023
概括
本研究介绍了资源意识视频流 (RAViS) 框架,以优化对象检测在像Raspberry Pi这样的有限硬件上. 该框架将深度学习模型适应可用的资源,确保视频流挖掘的连续运行和准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 视频流挖掘需要大量的计算资源,往往超过有限的硬件的能力.
- 分析连续的视频数据流可能会导致系统停机,并由于资源限制而增加运营成本.
研究的目的:
- 开发一个资源意识的视频流 (RAViS) 框架,用于在资源有限的设备上高效地检测对象.
- 将基于深度学习的对象检测系统,特别是YOLO,适应可用的CPU,RAM和Raspberry Pi的存储.
主要方法:
- 开发了RAViS框架,以根据实时资源可用性监测和调整对象检测系统参数.
- 利用视频流模拟来测试框架在使用深度学习模型识别房间里的人的性能.
- 实施了适应性策略,以优化用于连续视频处理的资源利用.
主要成果:
- RAViS框架成功地将YOLO物体检测系统适应了树派的有限资源.
- 实验结果表明,该框架在资源限制范围内运行时保持了检测准确性.
- 该系统在有限的硬件上确保了对象检测任务的连续运行.
结论:
- RAViS框架使物体检测系统能够在资源有限的计算机上有效运行.
- 持续监控和反机制允许动态调整检测参数,优化资源利用.
- 这种方法确保了基于深度学习的对象检测在流式视频分析中的持续准确性和有效性.
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