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首页> 外文期刊>Journal of visual communication & image representation >A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation
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A multi-resolution approach for massively-parallel hardware-friendly optical flow estimation

机译：大规模并行的硬件友好型光流估计的多分辨率方法

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This paper presents a novel hardware-friendly motion estimation for real-time applications such as robotics or autonomous navigation. Our approach is based on the well-known Lucas & Kanade local algorithm, whose main problem is the unreliability of its estimations for large-range displacements. This disadvantage is solved in the literature by adding the sequential multiscale-with-warping extension, although it dramatically increases the computational cost. Our choice is the implementation of a multi-resolution scheme that avoids the warping computation and allows the estimation of large-range motion. This alternative allows the parallel computation of the scale-by-scale motion estimation which makes the whole computation lighter and significantly reduces the processing time compared with the multiscale-with-warping approach. Furthermore, this last fact also means reducing the hardware resource cost for its potential implementation in digital hardware devices such as CPUs, ASICs, or FPGAs. In the discussion, we analyze the speedup of the multiresolution approach compared to the multiscale-with-warping scheme. For an FPGA implementation, we obtain a reduction of latency between 40% and 50% and a resource reduction of 30%. The final solution copes with large-range motion estimations with a simplified architecture very well-suited for customized digital hardware datapath implementations as well as current multicore architectures.
机译：本文提出了一种针对实时应用（例如机器人技术或自主导航）的新颖的硬件友好型运动估计。我们的方法基于著名的Lucas＆Kanade局部算法，其主要问题是其对大范围位移的估计不可靠。尽管增加了计算成本，但是通过添加具有翘曲的连续多尺度解决了该缺点。我们的选择是实现多分辨率方案，该方案避免了翘曲计算并允许进行大范围运动的估计。与带变形的多尺度方法相比，该替代方案允许按比例缩放运动估计的并行计算，这使整个计算更轻松，并显着减少了处理时间。此外，最后一个事实还意味着降低其在数字硬件设备（例如CPU，ASIC或FPGA）中潜在实现的硬件资源成本。在讨论中，我们分析了多分辨率方法与多尺度扭曲方法相比的加速。对于FPGA实现，我们将等待时间减少了40％至50％，并将资源减少了30％。最终的解决方案通过非常适合定制数字硬件数据路径实现以及当前多核体系结构的简化体系结构来应对大范围运动估计。

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