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联合深度超参数卷积和交叉关联注意力的大位移光流估计

王梓歌 葛利跃 陈震 张聪炫 王子旭 舒铭奕

王梓歌, 葛利跃, 陈震, 张聪炫, 王子旭, 舒铭奕. 联合深度超参数卷积和交叉关联注意力的大位移光流估计. 自动化学报, 2024, 50(7): 1−15 doi: 10.16383/j.aas.c230049
引用本文: 王梓歌, 葛利跃, 陈震, 张聪炫, 王子旭, 舒铭奕. 联合深度超参数卷积和交叉关联注意力的大位移光流估计. 自动化学报, 2024, 50(7): 1−15 doi: 10.16383/j.aas.c230049
Wang Zi-Ge, Ge Li-Yue, Chen Zhen, Zhang Cong-Xuan, Wang Zi-Xu, Shu Ming-Yi. Large displacement optical flow estimation jointing depthwise over-parameterized convolution and cross correlation attention. Acta Automatica Sinica, 2024, 50(7): 1−15 doi: 10.16383/j.aas.c230049
Citation: Wang Zi-Ge, Ge Li-Yue, Chen Zhen, Zhang Cong-Xuan, Wang Zi-Xu, Shu Ming-Yi. Large displacement optical flow estimation jointing depthwise over-parameterized convolution and cross correlation attention. Acta Automatica Sinica, 2024, 50(7): 1−15 doi: 10.16383/j.aas.c230049

联合深度超参数卷积和交叉关联注意力的大位移光流估计

doi: 10.16383/j.aas.c230049
基金项目: 国家自然科学基金(62222206, 62272209), 江西省重大科技研发专项(20232ACC01007), 江西省重点研发计划重点专项(20232BBE50006), 江西省技术创新引导类计划项目(2021AEI91005), 江西省教育厅科学技术项目(GJJ210910), 江西省图像处理与模式识别重点实验室开放基金(ET202104413)资助
详细信息
    作者简介:

    王梓歌:南昌航空大学测试与光电工程学院硕士研究生. 主要研究方向为计算机视觉. E-mail: Wangzggg@163.com

    葛利跃:南昌航空大学信息工程学院助理实验师. 北京航空航天大学仪器科学与光电工程学院博士研究生. 主要研究方向图像检测与智能识别. E-mail: lygeah@163.com

    陈震:南昌航空大学测试与光电工程学院教授. 2003年获得西北工业大学博士学位. 主要研究方向为图像处理与计算机视觉. E-mail: dr_chenzhen@163.com

    张聪炫:南昌航空大学测试与光电工程学院教授. 2014年获得南京航空航天大学博士学位. 主要研究方向为图像处理与计算机视觉. 本文通信作者. E-mail: zcxdsg@163.com

    王子旭:南昌航空大学测试与光电工程学院硕士研究生. 主要研究方向为计算机视觉. E-mail: wangzixu0827@163.com

    舒铭奕:南昌航空大学测试与光电工程学院硕士研究生. 主要研究方向为计算机视觉. E-mail: shumingyi1997@163.com

Large Displacement Optical Flow Estimation Jointing Depthwise Over-parameterized Convolution and Cross Correlation Attention

Funds: Supported by National Natural Science Foundation of China (62222206, 62272209), National Science and Technology Major Project of Jiangxi Province (20232ACC01007), Key Research and Development Program of Jiangxi Province (20232BBE50006), the Technological Innovation Guidance Program of Jiangxi Province (2021AEI91005), Science and Technology Program of Education Department of Jiangxi Province (GJJ210910), and the Open Fund of Jiangxi Key Laboratory for Image Processing and Pattern Recognition (ET202104413)
More Information
    Author Bio:

    WANG Zi-Ge Master student at the School of Measuring and Optical Engineering, Nanchang Hangkong University. Her main research interest is computer vision

    GE Li-Yue Assistant experimenter at the School of Information Engineering, Nanchang Hangkong University. Ph.D. candidate at the School of Instrumentation and Optoelectronic Engineering, Beihang University. His research interest covers image detection and intelligent recognition

    CHEN Zhen Professor at the School of Measuring and Optical Engineering, Nanchang Hangkong University. He received his Ph.D. degree from Northwestern Polytechnical University in 2003. His research interest covers image processing and computer vision

    ZHANG Cong-Xuan Professor at the School of Measuring and Optical Engineering, Nanchang Hangkong University. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 2014. His research interest covers image processing and computer vision. Corresponding author of this paper

    WANG Zi-Xu Master student at the School of Measuring and Optical Engineering, Nanchang Hangkong University. His main research interest is computer vision

    SHU Ming-Yi Master student at the School of Measuring and Optical Engineering, Nanchang Hangkong University. His main research interest is computer vision

  • 摘要: 针对现有深度学习光流估计模型在大位移场景下的准确性和鲁棒性问题, 提出了一种联合深度超参数卷积和交叉关联注意力的图像序列光流估计方法. 首先, 通过联合深层卷积和标准卷积构建深度超参数卷积以替代普通卷积, 提取更多特征并加快光流估计网络训练的收敛速度, 在不增加网络推理量的前提下提高光流估计的准确性; 然后, 设计基于交叉关联注意力的特征提取编码网络, 通过叠加注意力层数获得更大的感受野, 以提取多尺度长距离上下文特征信息, 增强大位移场景下光流估计的鲁棒性; 最后, 采用金字塔残差迭代模型构建联合深度超参数卷积和交叉关联注意力的光流估计网络, 提升光流估计的整体性能. 分别采用MPI-Sintel和KITTI测试图像集对本文方法和现有代表性光流估计方法进行综合对比分析, 实验结果表明本文方法取得了较好的光流估计性能, 尤其在大位移场景下具有更好的估计准确性与鲁棒性.
  • 图  1  基于深度超参数卷积和交叉关联注意力的大位移光流估计网络示意图

    Fig.  1  Structure diagram of large displacement optical flow estimation based on depthwise over-parameterized convolution and cross correlation attention

    图  2  深度超参数卷积和标准卷积示意图

    Fig.  2  The structure diagram of conventional convolution and depthwise over-parameterized convolution

    图  3  深度超参数卷积操作

    Fig.  3  The operation of depthwise over-parameterized convolution

    图  4  不同光流模型特征图对比

    Fig.  4  Comparison of feature maps of different optical flow models

    图  5  交叉关联注意力机制

    Fig.  5  The cross correlation attention block

    图  6  基于交叉关联注意力的光流特征编码网络示意图

    Fig.  6  Structure diagram of optical flow feature encoder network based on cross correlation attention

    图  7  不同光流模型估计结果对比

    Fig.  7  Comparison of results of different optical flow models

    图  8  Clean和Final数据集不同序列特征图可视化 (其中红框区域内为存在明显区别的边缘特征信息结果)

    Fig.  8  Visualization of feature maps of different sequence in Clean and Final datasets (The red bounding box contains edge feature information results with significant differences)

    图  9  金字塔不同层数下不同尺度目标特征可视化

    Fig.  9  Visualization of Feature maps at different scales under different layers of pyramid

    图  10  MPI-Sintel测试集图像序列对比方法光流估计可视化结果

    Fig.  10  Flow field results of the comparable methods evaluated on some MPI-Sintel test datasets

    图  11  KITTI2015测试集图像序列对比方法光流估计误差可视化结果

    Fig.  11  Flow error maps of the comparable methods tested on KITTI2015 datasets

    图  12  Baseline_deconv在各数据集训练过程

    Fig.  12  The training process of Baseline_deconv on each dataset

    图  13  消融模型光流估计结果在MPI-Sintel测试数据集可视化对比

    Fig.  13  Comparison of visualization results of each ablation model on MPI-Sintel test datasets

    图  14  消融模型光流估计结果在KITTI2015测试数据集可视化对比

    Fig.  14  Comparison of visualization results of each ablation model on KITTI2015 datasets

    表  1  MPI-Sintel数据集图像序列光流估计结果 (pixels)

    Table  1  Optical flow calculation results of image sequences in MPI-Sintel dataset (pixels)

    CleanFinal
    对比方法AllMatchedUnmatchedAllMatchedUnmatched
    IRR-PWC[14]3.8441.47223.2204.5792.15424.355
    PPAC-HD3[36]4.5891.50729.7514.5992.11624.852
    LiteFlowNet2[37]3.4831.38320.6374.6862.24824.571
    IOFPL-ft[38]4.3941.61127.1284.2241.95622.704
    PWC-Net[25]4.3861.71926.1665.0422.44526.221
    HMFlow[39]3.2061.12220.2105.0382.40426.535
    SegFlow153[40]4.1511.24627.8556.1912.94032.682
    SAMFL[41]4.4771.76326.6434.7652.28225.008
    本文方法2.7631.06216.6564.2022.05621.696
    下载: 导出CSV

    表  2  数据集运动边缘与大位移指标对比结果 (pixels)

    Table  2  Comparison results of motion edge and large displacement index in MPI-Sintel dataset (pixels)

    CleanFinal
    对比方法${d}_{0\text{-}10}$${d}_{10\text{-}60}$${d}_{60\text{-}140}$${s}_{0\text{-}10}$${s}_{10\text{-}40}$${s}_{40+}$${d}_{0\text{-}10}$${d}_{10\text{-}60}$${d}_{60\text{-}140}$${s}_{0\text{-}10}$${s}_{10\text{-}40}$${s}_{40+}$
    IRR-PWC[14]3.5091.2960.7210.5351.72425.4304.1651.8431.2920.7092.42328.998
    PPAC-HD3[36]2.7881.3401.0680.3551.28933.6243.5211.7021.6370.6172.08330.457
    LiteFlowNet2[37]3.2931.2630.6290.5971.77221.9764.0481.8991.4730.8112.43329.375
    IOFPL-ft[38]3.0591.4210.9430.3911.29231.8123.2881.4791.4190.6461.89727.596
    PWC-Net[25]4.2821.6570.6740.6062.07028.7934.6362.0871.4750.7992.98631.070
    HMFlow[39]2.7860.9570.5840.4671.69320.4704.5822.2131.4650.9263.17029.974
    SegFlow153[40]3.0721.1430.6560.4862.00027.5634.9692.4922.1191.2013.86536.570
    SAMFL[41]3.9461.6230.8110.6181.86029.9954.2081.8461.4490.8932.58729.232
    本文方法2.7720.8540.4430.5411.62116.5753.8841.6601.2920.7532.38125.715
    下载: 导出CSV

    表  3  KITTI2015数据集计算结果 (%)

    Table  3  Calculation results in KITTI2015 dataset (%)

    对比方法$Fl\text{-}bg $$Fl\text{-}fg $$Fl\text{-}all $
    IRR-PWC[14]7.687.527.65
    PPAC-HD3[36]5.787.486.06
    LiteFlowNet2[37]7.627.647.62
    IOFPL-ft[38]6.52
    PWC-Net[25]9.66 9.319.60
    SegFlow153[40]22.2123.7222.46
    SAMFL[41]7.727.437.68
    本文方法7.436.657.30
    下载: 导出CSV

    表  4  MPI-Sintel数据集上消融实验结果对比 (pixels)

    Table  4  Comparison of ablation experiment results in MPI-Sintel dataset (pixels)

    消融模型AllMatchedUnmatched$s_{10\text{-}40}$$s_{40+}$
    Baseline3.8441.47223.2201.72425.430
    Baseline_CS2.8921.07017.7651.66217.460
    Baseline_deconv3.6211.46121.2721.65923.482
    Full model2.7631.06216.6561.62116.575
    下载: 导出CSV

    表  5  KITTI2015数据集上消融实验结果对比

    Table  5  Comparison of ablation experiment results in KITTI2015 dataset

    消融模型Fl-bg (%)Fl-fg (%)Fl-all (%)训练时间(min)
    Baseline7.687.527.65621
    Baseline_CS7.747.587.71690
    Baseline_deconv7.287.307.29632
    Full model7.436.657.30616
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-02-10
  • 录用日期:  2023-08-29
  • 网络出版日期:  2023-10-07

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