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.