Sparse-to-dense Large Displacement Motion Optical Flow Estimation Based on Deep Matching
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摘要: 针对非刚性大位移运动场景的光流计算准确性与鲁棒性问题, 提出一种基于深度匹配的由稀疏到稠密大位移运动光流估计方法. 首先利用深度匹配模型计算图像序列相邻帧的初始稀疏运动场; 其次采用网格化邻域支持优化模型筛选具有较高置信度的图像网格和匹配像素点, 获得鲁棒的稀疏运动场; 然后对稀疏运动场进行边缘保护稠密插值, 并设计全局能量泛函优化求解稠密光流场. 最后分别利用MPI-Sintel和KITTI数据库提供的测试图像集对本文方法和Classic + NL, DeepFlow, EpicFlow以及FlowNetS等变分模型、匹配策略和深度学习光流计算方法进行综合对比与分析, 实验结果表明本文方法相对于其他方法具有更高的光流计算精度, 尤其在非刚性大位移和运动遮挡区域具有更好的鲁棒性与可靠性.Abstract: In order to improve the accuracy and robustness of optical flow estimation under the non-rigid large displacement motion, we propose a sparse-to-dense large displacement motion optical flow estimation method based on deep matching. First, we utilize the deep matching model to compute an initial sparse motion field from the consecutive two frames of the image sequence. Second, we adopt the gridded neighborhood support optimization scheme to extract the image grids and matching pixels which have the high confidence, and acquire the robust sparse motion field. Third, we interpolate the sparse motion field to obtain the dense motion field and plan a global energy function to estimate the optimized dense optical flow. Finally, we respectively employ the MPI-Sintel and KITTI datasets to compare the performance of the proposed method with several variational, region matching and deep-learning based optical flow approaches including Classic + NL, DeepFlow, EpicFlow and FlowNetS models. The experimental results indicate that the proposed method has the higher computational accuracy compared with those of the other state-of-the-art approaches, especially owns the better robustness and reliability in the areas of non-rigid large displacements and motion occlusions.
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Key words:
- Dense optical flow /
- deep matching /
- neighborhood support /
- image grid /
- global optimization
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图 1 基于区域划分的深度匹配采样窗口示意图 ((a)参考帧采样窗口; (b)传统匹配方法采样窗口;(c)深度匹配算法采样窗口)
Fig. 1 Illustration of the deep matching sample window based on the regional division ((a) Sample window of the reference frame; (b) Sample window of the traditional matching method; (c) Sample window of the deep matching method)
图 2 深度匹配金字塔采样示意图 ((a)第1帧子区域聚合; (b)第2帧子区域聚合)
Fig. 2 Illustration of the pyramid sampling based deep matching ((a) Subregion polymerization of the first frame; (b) Subregion polymerization of the second frame)
图 3 本文邻域支持模型运动场优化效果 (蓝色标记符表示匹配正确像素点, 红色标记符表示匹配错误像素点)
Fig. 3 Optimization effect of the motion field by using the proposed neighborhood supporting model (The blue mark indicates the correct matching pixels, the red mark denotes the false matching pixels)
图 4 不同参数设置对本文光流估计精度的影响
Fig. 4 Variation of optical flow estimation results respect to different parameters
图 5 非刚性大位移与运动遮挡图像序列光流估计结果
Fig. 5 Optical flow results of the image sequences including non-rigidly large displacements and motion occlusions
图 7 MPI-Sintel数据库消融实验光流图
Fig. 7 Optical flow results of the ablation experiment tested on MPI-Sintel database
表 1 MPI-Sintel数据库光流估计误差对比
Table 1 Comparison results of optical flow errors on MPI-Sintel database
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表 2 非刚性大位移与运动遮挡图像序列光流估计误差对比
Table 2 Comparison results of optical flow errors on the image sequences including non-rigidly large displacements and motion occlusions
对比方法 平均误差 Ambush_5 Cave_2 Market_2 Market_5 Temple_2 AAE/AEE AAE/AEE AAE/AEE AAE/AEE AAE/AEE AAE/AEE Classic+NL[7] 14.71/9.28 22.53/11.06 15.78/14.03 7.64/0.98 18.93/16.59 8.39/3.72 DeepFlow[18] 10.89/6.66 18.86/8.75 9.23/9.30 8.00/0.85 12.19/11.89 6.15/2.50 EpicFlow[21] 10.64/6.47 19.19/8.48 7.45/7.81 7.91/0.89 12.15/12.47 6.48/2.72 FlowNetS[12] 15.63/9.77 25.37/12.43 17.24./15.66 8.56/1.26 16.56/15.24 10.45/4.24 本文方法 9.77/6.12 18.43/8.43 6.98/7.49 7.05/0.78 10.58/11.35 5.83/2.56
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表 4 本文方法消融实验结果对比
Table 4 Comparison results of the ablation experiment
消融模型 Alley_2 Cave_4 Market_6 本文方法 0.07 1.16 3.72 无匹配优化 0.09 1.28 5.07 无稠密插值 0.14 1.31 5.85 无全局优化 0.09 1.21 3.84
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