浏览全部资源
扫码关注微信
收稿：2018-10-19，
修回：2019-2-22，
纸质出版：2019-08-16
移动端阅览
目的
2
云台相机因监控视野广、灵活度高，在高速公路监控系统中发挥出重要的作用，但因云台相机焦距与角度不定时地随监控需求变化，对利用云台相机的图像信息获取真实世界准确的物理信息造成一定困难，因此进行云台相机非现场自动标定方法的研究对高速公路监控系统的应用具有重要价值。
方法
2
本文提出了一种基于消失点约束与车道线模型约束的云台相机自动标定方法，以建立高速公路监控系统的图像信息与真实世界物理信息之间准确描述关系。首先，利用车辆目标运动轨迹的级联霍夫变换投票实现纵向消失点的准确估计，其次以车道线模型物理度量为约束，并采用枚举策略获取横向消失点的准确估计，最终在已知相机高度的条件下实现高速公路云台相机标定参数的准确计算。
结果
2
将本文方法在不同的场景下进行实验，得到在不同的距离下的平均误差分别为4.63%、4.74%、4.81%、4.65%，均小于5%。
结论
2
对多组高速公路监控场景的测试实验结果表明，本文提出的云台相机自动标定方法对高速公路监控场景的物理测量误差能够满足应用需求，与参考方法相比较而言具有较大的优势和一定的应用价值，得到的相机内外参数可用于计算车辆速度与空间位置等。
Objective
2
In the field of image processing
camera calibration is used to determine the relationship between the 3D geometric position of a point in space and the corresponding point in an image. Camera calibration mainly aims to obtain the camera's intrinsic
extrinsic
and distortion parameters. The intrinsic and extrinsic parameters of the camera can be used to calculate vehicle speed and spatial location
and detect and recognize traffic events
among others. Recently
pan-tilt-zoom (PTZ) cameras have been playing an important role in highway monitoring systems due to their wide field of view and high flexibility. The image obtained by the PTZ camera is changed as the focal length and angles of the PTZ camera change with demand
which makes obtaining the camera parameters difficult. Therefore
research on the autocalibration method of the PTZ camera has an important application value in the highway intelligent monitoring system. The calibration of the PTZ camera is mainly based on vanishing points. A set of parallel lines in space intersect on a point in the image through the perspective transformation of the camera. The intersection point is the vanishing point. Three vanishing points that are orthogonal to each other can be formed in the 3D space. According to the number of vanishing points
the calibration method is divided into two categories:based on double (VVH
VVW
VVL) and single vanishing point (VWH
VLH
VWL). V denotes a vanishing point
W denotes the distance between the two-lane lines on the road
L denotes the length of the lane line on the road
and H denotes the height of the camera.
Method
2
The PTZ camera has two characteristics:the roll angle is zero
and the principal point is at the center of the image. Therefore
the camera model can be reasonably simplified based on the above characteristics. Determining the focal length
the pan angle
the tilt angle
and the height of the camera is necessary to obtain the intrinsic and extrinsic parameters of the camera. In an actual highway scene
the height of the camera is known. This study proposes a PTZ camera autocalibration method based on two vanishing point constraints and lane line model constraint
which belongs to the VVH method. First
the SSD algorithm is used to detect the vehicle objects
and the optical flow method is used to track the vehicle objects to obtain the object trajectory set. Given that the road is partially curved
each trajectory needs to be processed to obtain a trajectory set that conforms to the linear features. The linear trajectories in the trajectory set are voted in the cascaded Hough transform space to obtain a longitudinal vanishing point. Second
in the actual highway scene
the vehicle object is relatively small in the image because of the high camera height. Therefore
the general method of obtaining a second vanishing point by detecting the edge of the vehicle is not applicable in the scene. No other parallel lines can be used in the scene to directly obtain the second vanishing point. Therefore
taking the physical metric of the lane line model as the constraint
the enumeration strategy is used to obtain the estimated value of the horizontal vanishing point. Finally
an accurate calculation of the calibration parameters of the PTZ camera is achieved under the condition of known camera height.
Result
2
The proposed autocalibration of the PTZ camera is performed in different scenes of multiple highways in Zhejiang Province. Videos corresponding to these scenes have standard and high definitions. The height of the PTZ camera in all scenes is 13.0 m. This study used the differences between the actual physical distance of the lane line in the scene and its test distance as the measure of the calibration error. The average errors at different distances were 4.80%
4.55%
4.78%
and 4.99%. The proposed method cannot be compared with the existing autocalibration because of different error measures. However
this study uses the same scene for manual calibration to prove that the proposed algorithm is reasonable and has certain practical values. The average error of manual calibration is about 2%. The autocalibration results are not as good as the manual calibration results because no artificial intervention exists. However
the average errors of autocalibration are less than 5%.
Conclusion
2
The proposed method has the following advantages:1) It makes full use of the characteristics of the PTZ camera to simplify the camera model and calibration process. 2) It optimizes the vehicle's motion trajectory set and uses the cascaded Hough transform to obtain the longitudinal vanishing point stably and accurately. 3) It makes full use of various kinds of markers in the scene and proposes a method of enumerating tentative acquisition to obtain horizontal vanishing points. Experimental results show that the algorithm could meet application requirements. The obtained camera parameters can also be used in other scenes
such as traffic parameter calculation and vehicle classification. The method of extracting lane lines by Hough transform is susceptible to light. Therefore
we can change the method of extracting lane lines to improve calibration accuracy.
Faig W. Calibration of close-range photogrammetry systems:mathematical formulation[J]. Photogrammetric Engineering Remote Sensing, 1975, 41(12):1479-1486.
Tsai R Y. An efficient and accurate camera calibration technique for 3D machine vision[C]//Proceedings of 1986 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 1986: 364-374.
Zhang Z. 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]
Maybank S J, Faugeras O D. A theory of self-calibration of a moving camera[J]. International Journal of Computer Vision, 1992, 8(2):123-151.[DOI:10.1007/BF00127171]
Hartley R I. Kruppa's equations derived from the fundamental matrix[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(2):133-135.[DOI:10.1109/34.574792]
Ma S D. A self-calibration technique for active vision systems[J]. IEEE Transactions on Robotics and Automation, 1996, 12(1):114-120.[DOI:10.1109/70.481755]
Trarajković M. Interactive calibration of a PTZ camera for surveillance applications[C]//Proceedings of the 5th Asian Conference on Computer Vision. Melbourne, Austrakia: ACCV, 2002.
Bas E K, Crisman J D. An easy to install camera calibration for traffic monitoring[C]//Proceedings of Conference on Intelligent TransportationSystems. Boston, MA, USA: IEEE, 1997: 362-366.[ DOI: 10.1109/ITSC.1997.660502 http://dx.doi.org/10.1109/ITSC.1997.660502 ]
Lai A H S, Yung N H C. Lane detection by orientation and length discrimination[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000, 30(4):539-548.[DOI:10.1109/3477.865171]
He X C, Yung N H C. New method for overcoming ill-conditioning in vanishing-point-based camera calibration[J]. Optical Engineering, 2007, 46(3):#037202.[DOI:10.1117/1.2714991]
Schoepflin T N, Dailey D J. Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation[J]. IEEE Transactions on Intelligent Transportation Systems, 2003, 4(2):90-98.[DOI:10.1109/TITS.2003.821213]
Song K T, Tai J C. Dynamic calibrationof pan-tilt-zoom cameras for traffic monitoring[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2006, 36(5):1091-1103.[DOI:10.1109/TSMCB.2006.872271]
Kanhere N K, Birchfield S T. A taxonomy and analysis of camera calibration methods for traffic monitoring applications[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(2):441-452.[DOI:10.1109/TITS.2010.2045500]
Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 21-37.
Bouguet J Y. Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm[R]. Santa Clara: Intel Corporation, Microprocessor Research Labs, 1999: 363-381.[ DOI: 10.5244/c.27.90 http://dx.doi.org/10.5244/c.27.90 ]
DubskáM, Herout A. Real projective plane mapping for detection of orthogonal vanishing points[C]//Proceedings of the British Machine Vision Conference. Bristol: BMVC, 2013: 90.1-90.11.[ DOI: 10.111711.2714991 http://dx.doi.org/10.111711.2714991 ]
相关作者
相关机构
京公网安备11010802024621
