基于fpga的高能效密集光流计算的C语言实现(仅摘要)

Zhibin Wang, Wenmin Yang, Jin Yu, Zhilei Chai
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摘要

光流计算广泛应用于许多基于视频/图像的应用,如运动检测、视频压缩等。密集的光流场提供了更多的细节信息,在许多应用中更为有用。然而,用于密集光流计算的高质量算法在计算上是昂贵的。例如,在ZYNQ的ARM Cortex-A9处理器上,当图像尺寸为640 x 480时,流行的线性变分方法组合亮度梯度(CBG)每帧花费26.68美元来计算光流。特别是在考虑功率限制的嵌入式系统时,很难加快速度。可移植性差是限制当前光流计算实现在更多应用中使用的另一个因素。本文提出了一种高性能、低功耗的密集光流计算fpga加速实现方案。实现了一种高质量的密集光流方法——组合亮度-梯度模型。使用C代码代替VHDL/Verilog HDL来提高生产率。系统的可移植性经过精心设计,便于在不同平台上部署。实验结果表明,当使用640 × 480图像并计算所有像素的光流时,该系统可实现12 fps和0.38J /帧的光流计算。此外,通过在不同的异构平台(如ZYNQ-7000 SoC和使用Kintex-7 FPGA的PC-FPGA平台)上实现光流算法,证明了该算法的可移植性。
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Implementing FPGA-based energy-efficient dense optical flow computation with high portability in C (abstract only)
Optical flow computation is widely used in many video/image based applications such as motion detection, video compression etc. Dense optical flow field that provides more details of information is more useful in lots of applications. However, high-quality algorithms for dense optical flow computation are computationally expensive. For instance, on the ARM Cortex-A9 processor within ZYNQ, the popular linear variational method Combine-Brightness-Gradient (CBG), spends $26.68s per frame to compute optical flow when the image size is 640 x 480. It is difficult to be sped up especially when embedded systems with power constraints are considered. Poor portability is another factor to limit current implementations of optical flow computation to be used in more applications. In this paper, a high-performance, low-power FPGA-accelerated implementation of dense optical flow computation is presented. One high-quality dense optical flow method, the Combine-Brightness-Gradient model, is implemented. C code instead of VHDL/Verilog HDL is used to improve the productivity. Portability of the system is designed carefully for deploying it on different platforms conveniently. Experimental results show 12 fps and 0.38J per frame are achieved by this optical flow computing system when 640 x 480 image is used and optical flow for all pixels are computed. Furthermore, portability is demonstrated by implementing the optical flow algorithm on different heterogeneous platforms such as the ZYNQ-7000 SoC and the PC-FPGA platform with a Kintex-7 FPGA respectively.
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