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在嵌入式系统上的低分辨率红外阵列传感器的帮助下,减少了用于人类姿势检测的CPU工作量
Marcos G Alves1, Gen-Lang Chen1, Xi Kang1
1School of Computing and Data Engineering, NingboTech University, Ningbo 315100, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了一种热传感器控制的系统,以优化嵌入式计算机视觉. 通过仅在有人在场时激活姿势检测,它可以显著降低CPU负载和功耗.
科学领域:
- 计算机视觉 计算机视觉
- 嵌入式系统 嵌入式系统
- 机器学习 机器学习
背景情况:
- 现代嵌入式系统为边缘计算和计算机视觉任务 (如姿势检测) 提供了相当大的处理能力.
- 现有的框架 (例如,MediaPipe) 消耗大量的CPU资源,导致浪费电力和不必要的数据生成,即使没有活跃主体.
- 当前系统中的错误检测进一步加剧了效率低下和资源流失.
研究的目的:
- 为嵌入式应用开发一个高效的混合计算机视觉系统.
- 为了减少CPU工作负载和姿势检测算法的功耗.
- 在低活动环境中尽量减少错误检测.
主要方法:
- 使用低成本的红外热传感器阵列来触发姿势检测.
- 开发了一种轻量级的算法,用于在热图像中检测和隔离人.
- 集成了热检测系统与MediaPipe在单板计算机上的姿势检测.
主要成果:
- 实现了平均CPU工作负载的显著减少,特别是在低活动场景中.
- 通过控制基于热输入的执行,有效消除了MediaPipe的错误检测.
- 在最佳条件下,已证明可节省高达30%的电力.
结论:
- 使用热传感来控制计算机视觉算法的混合方法对嵌入式系统非常有效.
- 这种方法优化了资源利用,降低了功耗,提高了系统效率.
- 该系统为节能监控和安全应用提供了实用解决方案.
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