基于断面水边线定向的视频测流摄像机标定

张振; 姜天生; 赵丽君; 程泽

doi:10.11999/JEIT230573

基于断面水边线定向的视频测流摄像机标定

doi: 10.11999/JEIT230573

河海大学计算机与信息学院南京 211100

基金项目: 国家自然科学基金青年基金 (51709083)，国家重点研发计划(2017YFC0405703)

详细信息

作者简介:
张振：男，副教授，研究方向为图像法测流及水利智能感知技术

姜天生：男，硕士生，研究方向为自适应时空测速法测流技术

赵丽君：女，硕士生，研究方向为多传感器融合测流

程泽：男，硕士生，研究方向为基于图像法的灌渠测流系统研发

通讯作者:
张振　zz_hhuc@hhu.edu.cn

中图分类号: TN911.73; TH815
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- 被引次数: 0
出版历程
- 收稿日期: 2023-06-09
- 修回日期: 2023-09-27
- 网络出版日期: 2023-10-16
- 刊出日期: 2024-04-24

Camera Calibration Using Cross-Section Waterline Orientation for Video-based Flow Measurement

School of Computer and Information, Hohai University, Nanjing 211100, China

Funds: The Youth Foundation of National Natural Science Foundation of China (51709083), The National Key Research and Development Program (2017YFC0405703)

摘要

摘要: 针对现有基于直接线性变化法(DLT)的图像法测流技术依赖于地面控制点，存在效率低、风险高、宽断面天然河流操作难度大等问题，该文提出一种基于断面水边线定向的摄像机姿态角标定方法(CSWO)。该方法将标定过程分解为实验室内参标定和现场外参标定两步，其中后者又被划分为摄像机定位和定向两个环节。定向环节中首先在摄像机安装时将光轴与断面方向对齐，使方位角置零。然后利用无畸变图像中人工标注的平直断面水边线的斜率计算出横滚角。接下来通过计算水边线与图像纵轴交点作为其亚像素像方坐标。最后依据透视投影成像模型，联合水位与插值断面高程求得的物方坐标解算出俯仰角。该方法应用于时空图像测速法(STIV)，实现了200 m宽河流的免像控表面流速测量。结果表明：起点距的最大绝对误差为0.59 m，最大相对误差为0.45%，表面流速的最大相对误差小于6.3%。
- 表面流速测量 /
- STIV /
- 免地面控制点 /
- 摄像机标定 /
- 断面水边线
Abstract: The image-based water flow measurement technology based on the Direct Linear Transformation (DLT) method relies on ground control points, and has problems such as low efficiency, high risk, and difficult operation in natural rivers with wide sections. A camera attitude angle calibration method based on Cross-Section Waterline Orientation (CSWO) is proposed. In this method, the calibration process is divided into two steps: laboratory calibration and field calibration, and the latter is divided into camera positioning and orientation. In the orientation step, the optical axis is required to be aligned with the cross-section when the camera is installed, so that the azimuth angle is set to zero. The roll angle is calculated by the slope of the straight waterline marked manually in the undistorted image. Then the sub-pixel image coordinate of the intersection point between the waterline and the vertical axis of the image is calculated. Finally, the pitch angle is calculated according to the perspective projection imaging model combined with the object coordinates obtained by the water level and the interpolated elevation of cross-section. This method has been applied to Space-Time Image Velocimetry (STIV) to measure the image-free surface velocity of a river with a width of 200 m. The results show that the maximum absolute error of starting distance is 0.59 m, the maximum relative error is 0.45%, and the maximum relative error of surface velocity is less than 6.3%.
- Surface velocity measurement /
- STIV /
- GCP-free (Ground Comtrol Point-free) /
- Camera calibration /
- Cross-section waterline

HTML全文

图 1 算法整体流程图

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图 2 系统布设方案示意图

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图 3 水边线及标注点示意

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图 4 FFT-STIV原理示意

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图 5 标注场景示意图

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图 6 攀枝花水文站大断面

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图 7 测流系统布设示意图

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图 8 断面及流向示意图

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图 9 硬件系统结构框图

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图 10 测次15测速线起点距对比

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图 11 比测实验现场状况

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图 12 表面流速测量结果对比

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图 13 测速线9处STI

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表 1 内参矩阵和畸变参数

	$ m $(pixel)	$ n $(pixel)	$ {f_x} $(pixel)	$ {f_y} $(pixel)	$ {C_x} $(pixel)	$ {C_y} $(pixel)	$ {k_1} $	$ {k_2} $	$ {p_1} $	$ {p_2} $
标定结果	3840	2160	2876.507	2884.631	1947.382	1043.743	–0.407	0.001	0.001	–0.001

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表 2 各水位级下俯仰角、横滚角标定结果对比

测次	日期	时间	场景	水位级	水位(m)	$ R\left(x_{1}, y_{1}\right) $	$ R_{2}\left(x_{2}, y_{2}\right) $	俯仰角(°)	横滚角(°)
1	210407	15:00	晴	低	985.34	(990, 443)	(2071, 435)	20.16	0.42
2	210204	10:30	晴		986.19	(982, 445)	(2125, 441)	19.70	0.20
3	210416	20:05	夜		986.34	(984, 439)	(2127, 433)	19.79	0.30
4	210416	20:10	夜		986.34	(984, 439)	(2125, 431)	19.81	0.40
5	201118	10:30	阴		986.37	(960, 437)	(2151, 429)	19.84	0.39
6	201118	10:35	阴		986.37	(966, 437)	(2143, 431)	19.81	0.29
7	210415	14:18	晴		986.37	(984, 437)	(2141, 427)	19.87	0.50
8	210318	9:00	阴		986.40	(978, 437)	(2027, 427)	19.88	0.55
9	210414	13:00	晴		986.42	(984, 437)	(2141, 431)	19.79	0.30
10	210408	14:00	晴		986.42	(1006, 439)	(2079, 429)	19.82	0.53
11	210408	16:00	晴		986.62	(974, 437)	(2187, 431)	19.79	0.28
12	210316	9:00	阴		986.90	(978, 427)	(2143, 419)	19.81	0.39
13	210223	11:00	晴		987.03	(982, 423)	(2095, 415)	19.84	0.41
14	220704	9:35	阴	中	993.80	(1100, 257)	(2057, 255)	19.91	0.12
15	220704	9:20	阴		993.84	(1096, 257)	(2073, 255)	19.89	0.12
16	220704	9:26	阴		993.85	(1092, 257)	(2071, 255)	19.89	0.12
17	200821	10:00	阴	高	997.96	(1008, 206)	(1824, 202)	19.88	0.28

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表 3 DSO法和CSWO法实验结果

测次	水位(m)	起点距真值(m)	绝对误差(m)		相对误差(%)
测次	水位(m)	起点距真值(m)	DSO	CSWO	DSO	CSWO
1	985.34	55.00	0.29	0.02	0.22	0.02
2	986.19	90.00	0.64	0.12	0.51	0.09
3	986.34	65.00	0.24	0.18	0.18	0.14
4	986.34	90.00	0.81	0.02	0.62	0.01
5	986.37	55.00	0.13	0.17	0.10	0.13
6	986.37	65.00	0.64	0.23	0.50	0.18
7	986.37	90.00	0.79	0.01	0.61	0.01
8	986.40	120.00	1.64	0.27	1.27	0.21
9	986.42	65.00	0.34	0.09	0.26	0.07
10	986.42	105.00	0.94	0.13	0.72	0.10
11	986.62	135.00	1.18	0.59	0.90	0.45
12	986.90	65.00	0.26	0.28	0.19	0.21
13	987.03	90.00	0.72	0.19	0.55	0.15
14	993.80	90.00	0.97	0.29	0.57	0.17
15	993.84	55.00	0.42	0.05	0.25	0.03
16	993.85	65.00	0.66	0.00	0.39	0.00
17	997.96	55.00	0.31	0.28	0.17	0.15

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参考文献(24)

[1]	LE COZ J, HAUET A, PIERREFEU G, et al. Performance of Image-based Velocimetry (LSPIV) applied to flash-flood discharge measurements in mediterranean rivers[J]. Journal of Hydrology, 2010, 394(1/2): 42–52. doi: 10.1016/j.jhydrol.2010.05.049.
[2]	FUJITA I, WATANABE H, and TSUBAKI R. Development of a non-intrusive and efficient flow monitoring technique: The Space-Time Image Velocimetry (STIV)[J]. International Journal of River Basin Management, 2007, 5(2): 105–114. doi: 10.1080/15715124.2007.9635310.
[3]	TAURO F, TOSI F, MATTOCCIA S, et al. Optical Tracking Velocimetry (OTV): Leveraging optical flow and trajectory-based filtering for surface streamflow observations[J]. Remote Sensing, 2018, 10(12): 2010. doi: 10.3390/rs10122010.
[4]	张振, 徐枫, 王鑫, 等. 河流水面成像测速研究进展[J]. 仪器仪表学报, 2015, 36(7): 1441–1450. doi: 10.19650/j.cnki.cjsi.2015.07.001. ZHANG Zhen, XU Feng, WANG Xin, et al. Research progress on river surface imaging velocimetry[J]. Chinese Journal of Scientific Instrument, 2015, 36(7): 1441–1450. doi: 10.19650/j.cnki.cjsi.2015.07.001.
[5]	蒋海军. 大视场条件下的摄像机标定方法研究[D]. [硕士论文], 国防科技大学, 2017. doi: 10.27052/d.cnki.gzjgu.2017.000625. JIANG Haijun. Research on camera calibration method in large field of view[D]. [Master dissertation], National University of Defense Technology, 2017. doi: 10.27052/d.cnki.gzjgu.2017.000625.
[6]	张振, 徐枫, 沈洁, 等. 基于变高单应的单目视觉平面测量方法[J]. 仪器仪表学报, 2014, 35(8): 1860–1868. doi: 10.19650/j.cnki.cjsi.2014.08.025. ZHANG Zhen, XU Feng, SHEN Jie, et al. Plane measurement method with monocular vision based on variable-height homography[J]. Chinese Journal of Scientific Instrument, 2014, 35(8): 1860–1868. doi: 10.19650/j.cnki.cjsi.2014.08.025.
[7]	杨聃, 邵广俊, 胡伟飞, 等. 基于图像的河流表面测速研究综述[J]. 浙江大学学报:工学版, 2021, 55(9): 1752–1763. doi: 10.3785/j.issn.1008-973X.2021.09.017. YANG Dan, SHAO Guangjun, HU Weifei, et al. Review of image-based river surface velocimetry research[J]. Journal of Zhejiang University:Engineering Science, 2021, 55(9): 1752–1763. doi: 10.3785/j.issn.1008-973X.2021.09.017.
[8]	AL-MAMARI M M, KANTOUSH S A, KOBAYASHI S, et al. Real-time measurement of flash-flood in a Wadi area by LSPIV and STIV[J]. Hydrology, 2019, 6(1): 27. doi: 10.3390/hydrology6010027.
[9]	曹列凯, DETERT M, 李丹勋. 基于无人机的长河段表面流场测量系统与应用[J]. 清华大学学报:自然科学版, 2022, 62(12): 1922–1929. doi: 10.16511/j.cnki.qhdxxb.2022.21.012. CAO Liekai, DETERT M, and LI Danxun. Airborne image velocimetry system and its application to measure the surface flow fields of long river reaches[J]. Journal of Tsinghua University:Science and Technology, 2022, 62(12): 1922–1929. doi: 10.16511/j.cnki.qhdxxb.2022.21.012.
[10]	TSAI R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal on Robotics and Automation, 1987, 3(4): 323–344. doi: 10.1109/JRA.1987.1087109.
[11]	HOLLAND K T, HOLMAN R A, LIPPMANN T C, et al. Practical use of video imagery in nearshore oceanographic field studies[J]. IEEE Journal of Oceanic Engineering, 1997, 22(1): 81–92. doi: 10.1109/48.557542.
[12]	BECHLE A J, WU C H, LIU Wencheng, et al. Development and application of an automated river-estuary discharge imaging system[J]. Journal of Hydraulic Engineering, 2012, 138(4): 327–339. doi: 10.1061/(ASCE)HY.1943-7900.0000521.
[13]	HUANG Weiche, YOUNG C C, and LIU Wencheng. Application of an automated discharge imaging system and LSPIV during typhoon events in Taiwan[J]. Water, 2018, 10(3): 280. doi: 10.20944/preprints201802.0089.v1.
[14]	COSTA F A L and MITISHITA E A. An approach to improve direct sensor orientation using the integration of photogrammetric and lidar datasets[J]. International Journal of Remote Sensing, 2019, 40(14): 5651–5672. doi: 10.1080/01431161.2019.1580794.
[15]	ZHANG Zhen, ZHAO Lijun, LIU Boyuan, et al. Free-surface velocity measurement using direct sensor orientation-based STIV[J]. Micromachines, 2022, 13(8): 1167. doi: 10.3390/mi13081167.
[16]	LIU Wencheng and HUANG Weiche. Development of a three-axis accelerometer and Large-Scale Particle Image Velocimetry (LSPIV) to enhance surface velocity measurements in rivers[J]. Computers & Geosciences, 2021, 155: 104866. doi: 10.1016/j.cageo.2021.104866.
[17]	张振, 吕莉, 石爱业, 等. 基于物像尺度变换的河流水面流场定标方法[J]. 仪器仪表学报, 2017, 38(9): 2273–2281. doi: 10.19650/j.cnki.cjsi.2017.09.023. ZHANG Zhen, LV Li, SHI Aiye, et al. River surface flow field calibration method based on object-image scaling[J]. Chinese Journal of Scientific Instrument, 2017, 38(9): 2273–2281. doi: 10.19650/j.cnki.cjsi.2017.09.023.
[18]	TSUBAKI R. On the texture angle detection used in Space-Time Image Velocimetry (STIV)[J]. Water Resources Research, 2017, 53(12): 10908–10914. doi: 10.1002/2017WR021913.
[19]	FUJITA I, KOSAKA Y, and YOROZUYA A. Tracking of river surface features by space time imageing[C]. The 15th International Symposium on Flow Visualization, Minsk, Belarus, 2012: 25–28.
[20]	ZHANG Zhen, ZHOU Yang, LIU Haiyun, et al. Visual measurement of water level under complex illumination conditions[J]. Sensors, 2019, 19(19): 4141. doi: 10.3390/s19194141.
[21]	王慧斌, 董伟, 张振, 等. 基于时空图像频谱的时均流场重建方法[J]. 仪器仪表学报, 2015, 36(3): 623–631. doi: 10.19650/j.cnki.cjsi.2015.03.018. WANG Huibin, DONG Wei, ZHANG Zhen, et al. Time-averaged flow field reconstruction method based on spectrum of spatio-temporal image[J]. Chinese Journal of Scientific Instrument, 2015, 36(3): 623–631. doi: 10.19650/j.cnki.cjsi.2015.03.018.
[22]	ZHANG Zhengyou. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330–1334. doi: 10.1109/34.888718.
[23]	FUJITA I, NOTOYA Y, TANI K, et al. Efficient and accurate estimation of water surface velocity in STIV[J]. Environmental Fluid Mechanics, 2019, 19(5): 1363–1378. doi: 10.1007/s10652-018-9651-3.
[24]	赵浩源, 陈华, 刘维高, 等. 基于河流表面时空图像识别的测流方法[J]. 水资源研究, 2020, 9(1): 1–11. doi: 10.12677/JWRR.2020.91001. ZHAO Haoyuan, CHEN Hua, LIU Weigao, et al. Application of flow measurement method based on space-time image recognition of river surface[J]. Journal of Water Resources Research, 2020, 9(1): 1–11. doi: 10.12677/JWRR.2020.91001.