基于小波的稳健光流计算方法

王洪雁; 郑佳; 裴炳南

doi:10.11999/JEIT180077

基于小波的稳健光流计算方法

doi: 10.11999/JEIT180077

1.
大连大学辽宁省北斗高精度位置服务技术工程实验室大连 116622
2.
大连大学大连市环境感知与智能控制重点实验室大连 116622

基金项目: 国家自然科学基金(61301258, 61271379)，中国博士后科学基金(2016M590218)

详细信息

作者简介:
王洪雁：男，1979年生，副教授，博士，主要研究方向为MIMO雷达信号处理、毫米波通信、无人机控制

郑佳：男，1990年生，硕士生，研究方向为机器视觉、无人机容错控制

裴炳南：男，1956年生，教授，博士，博士生导师，主要研究方向为雷达信号处理、毫米波通信

通讯作者:
王洪雁　 gglongs@163.com

中图分类号: TN919.8
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- 被引次数: 0
出版历程
- 收稿日期: 2018-01-19
- 修回日期: 2018-09-18
- 网络出版日期: 2018-09-21
- 刊出日期: 2018-12-01

A Robust Optical Flow Calculation Method Based on Wavelet

1.
Liaoning Engineering Laboratory of BeiDou High-precision Location Service, Dalian University, Dalian 116622, China
2.
Dalian Key Laboratory of Environmental Perception and Intelligent Control, Dalian University, Dalian 116622, China

Funds: The National Natural Science Foundation of China (61301258, 61271379), China Postdoctoral Science Foundation (2016M590218)

摘要

摘要: 针对系统误差导致光流计算稳健性较差及精度较低的问题，该文提出一种基于小波多分辨理论的稳健光流计算方法。所提算法基于小波多尺度分辨率特性，将光照条件变化及传感器噪声引起的系统误差包含进光流计算中以改善光流计算的稳健性及估计精度，并通过总体最小二乘法求解超定小波光流方程组以获得光流矢量。仿真结果表明，与传统的Lucas-Kanade算法、Horn-Schunck算法及基于小波的全向图像光流估计方法相比，所提算法可显著改善光流估计精度及稳健性。
- 光流计算 /
- 小波多分辨 /
- 系统误差 /
- 总体最小二乘
Abstract: Focusing on the issue that the systematic errors lead to poor robustness and low accuracy of optical flow calculation, a robust optical flow calculation method is proposed in this paper, which is based on the wavelet multi-resolution theory. With the multi-resolution characteristics of wavelet, the system error caused by variation of illumination conditions and sensor noise is incorporated into the calculation of optical flow to improve the robustness and estimation accuracy. In what follows, the total least square method is used to solve the over-determined wavelet optical flow equations to obtain the optical flow vector. As compared to the traditional Lucas-Kanade approach, Horn-Schunck method and optical flow estimation in omnidirectional images using wavelet approach, simulation results show that the proposed algorithm can significantly improve the accuracy of optical flow estimation and the robustness of the optical flow field.
- Optical flow calculation /
- Wavelet multi-resolution /
- System error /
- Total least squares

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图 2 慢速运动场景下所得光流

下载: 全尺寸图片幻灯片

图 1 慢速运动场景下采集的连续4帧图像

下载: 全尺寸图片幻灯片

图 4 快速运动场景下所得光流

下载: 全尺寸图片幻灯片

图 3 快速运动场景下所采集的连续4帧图片

下载: 全尺寸图片幻灯片

图 5 不同运动场景下相邻2帧图像所得光流平均角度误差、平均角度标准差及绝对平均误差随迭代次数变化

下载: 全尺寸图片幻灯片

表 2 快速运动下光流性能参数

算法类型	E			F			H
算法类型	3帧、4帧	4帧、5帧	5帧、6帧	3帧、4帧	4帧、5帧	5帧、6帧	3帧、4帧	4帧、5帧	5帧、6帧
HS	12.23	12.20	12.27	12.59	12.54	12.57	0.91	0.93	0.90
LK	8.76	8.75	8.78	9.12	9.08	9.10	0.78	0.76	0.77
DC	4.89	4.78	4.82	4.35	4.33	4.36	0.34	0.36	0.35
本文算法	2.08	2.11	2.13	2.47	2.46	2.41	0.26	0.23	0.25

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表 1 慢速运动下光流性能参数

算法类型	E			F			H
算法类型	6帧、7帧	7帧、8帧	8帧、9帧	6帧、7帧	7帧、8帧	8帧、9帧	6帧、7帧	7帧、8帧	8帧、9帧
HS	11.56	11.48	11.51	12.07	11.98	12.05	0.79	0.76	0.80
LK	7.64	7.57	7.60	8.39	8.36	8.38	0.68	0.64	0.67
DC	3.21	3.19	3.34	3.45	3.42	3.47	0.34	0.36	0.32
本文算法	1.95	1.89	1.94	2.26	2.23	2.25	0.18	0.16	0.17

下载: 导出CSV

表 3 求解光流所需时间(s)

算法类型	慢速运动耗时			快速运动耗时
算法类型	6帧、7帧	7帧、8帧	8帧、9帧	3帧、4帧	4帧、5帧	5帧、6帧
HS光流法	4.42	4.38	4.45	5.58	5.39	5.45
LK光流法	4.14	4.03	4.18	4.67	4.31	4.46
DC光流法	3.23	3.19	3.42	3.53	3.42	3.47
本文算法	2.26	2.24	2.31	2.50	2.46	2.41

下载: 导出CSV

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