| 基于近似最近邻搜索的并行光流计算 |
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| 引用本文： | 杨昕欣,姜精萍.基于近似最近邻搜索的并行光流计算[J].计算机工程与应用,2018,54(18):201-207. |
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| 作者姓名： | 杨昕欣 姜精萍 |
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| 作者单位： | 北京航空航天大学 电子信息工程学院，北京 100191 |
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| 摘 要： | Barnes近似最近邻算法是当前匹配性能优秀的近似块匹配算法，将其应用于稠密光流的计算中，并与OpenCV中实现的两种稠密光流算法进行对比。针对Barnes算法不易并行化的不足，对Barnes算法中的传播过程进行修改，使其易于在GPU上实现并行加速。实验表明，经并行加速后的光流算法比原算法快两倍以上，而在精确度上与原算法接近，并且都优于OpenCV实现的两种稠密光流算法。
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| 关 键 词： | 光流 块匹配 并行加速 |
Parallel optical flow computing based on approximate nearest neighbor search |
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| YANG Xinxin,JIANG Jingping.Parallel optical flow computing based on approximate nearest neighbor search[J].Computer Engineering and Applications,2018,54(18):201-207. |
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| Authors: | YANG Xinxin JIANG Jingping |
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| Affiliation: | School of Electronic and Information Engineering, Beihang University, Beijing 100191, China |
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| Abstract: | Barnes approximate nearest neighbor algorithm is currently a well performance patch matching algorithm. This paper applies it in the compute of dense optical flow, and compares results with other two dense optical flow algorithms implemented by OpenCV. To make the Barnes algorithm easy to parallelize on GPU, the process of propagation is modified. Experimental results show that the parallel optical flow algorithm is more than two times faster than the original algorithm, the accuracy is nearly close to the original algorithm and better than the two dense optical flow algorithms implemented by OpenCV. |
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| Keywords: | optical flow patch match parallelize |
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