增强嵌入式对象跟踪:用于实时可预测性的硬件加速方法
Mingyang Zhang1, Kristof Van Beeck1, Toon Goedemé1
1PSI-EAVISE Research Group, Department of Electrical Engineering, KU Leuven, 2860 Sint-Katelijne-Waver, Belgium.
Journal of imaging
|March 27, 2024
概括
这项研究增强了使用现场可编程网关数组 (FPGA) 的嵌入式系统的实时对象跟踪. 硬件加速显著减少执行时间和延迟变化,以实现可预测的性能.
科学领域:
- 计算机视觉 计算机视觉
- 嵌入式系统工程 嵌入式系统工程
- 硬件加速器 硬件加速器
背景情况:
- 西安物体跟踪的进步还没有充分解决嵌入式设备上的硬实时可预测性.
- 对于许多嵌入式应用程序来说,最小和可预测的执行延迟非常重要.
- 基于深度学习的视频对象跟踪系统在实时性能方面面临挑战.
研究的目的:
- 在嵌入式系统上基于深度学习的视频对象跟踪中分析和改进实时可预测性.
- 在追踪系统中识别和解决时间可预测性瓶.
- 为了评估硬件加速对实时对象跟踪的有效性.
主要方法:
- 系统组件间实时可预测性的详细分析.
- 实施专门的硬件加速器,以使用高水平合成 (HLS) 进行深度智能交叉相关和填充.
- 在KV260嵌入式板上进行部署和测试.
主要成果:
- 现场可编程门阵列 (FPGA) 实现表现出优越的硬实时行为.
- 硬件加速导致平均执行时间的6.6倍加快.
- 与基线相比,延迟变化减少了11倍,显著提高了可预测性.
- 观察到提高了功率效率.
结论:
- 硬件加速对于在嵌入式系统上实现时间可预测的对象跟踪至关重要.
- 专门用于关键操作的硬件加速器有效地解决了性能瓶.
- 开发的方法为实时嵌入式视觉中的硬件-软件联合设计设定了新的标准.
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