Camera calibration methods include: traditional camera calibration method, active vision camera calibration method, camera self-calibration method.
The traditional camera calibration method needs to use a calibration object with known size, by establishing the correspondence between points with known coordinates on the calibration object and its image points, using a certain algorithm to obtain the internal and external parameters of the camera model. According to different calibration objects, it can be divided into three-dimensional calibration objects and planar calibration objects. The three-dimensional calibration object can be calibrated by a single image, and the calibration accuracy is high, but the processing and maintenance of high-precision three-dimensional calibration objects are difficult. The planar calibration object is easier to make than the three-dimensional calibration object, and the accuracy is easy to guarantee, but two or more images must be used for calibration. The traditional camera calibration method always needs a calibration object during the calibration process, and the manufacturing accuracy of the calibration object will affect the calibration result. At the same time, it is not suitable to place calibration objects in some occasions, which also limits the application of traditional camera calibration methods.
The current self-calibration algorithms mainly use the constraints of camera motion. The motion constraints of the camera are too strong, thus making it impractical in practice. The use of scene constraints mainly uses some parallel or orthogonal information in the scene. The intersection point of parallel lines on the camera image plane is called the vanishing point, which is a very important feature in projective geometry, so many scholars have studied the camera self-calibration method based on the vanishing point. The self-calibration method is flexible, and the camera can be calibrated online. But because it is a method based on absolute quadratic curves or surfaces, its algorithm has poor robustness.
The camera calibration method based on active vision refers to the calibration of the camera with certain motion information of the camera. This method does not require a calibration object, but it needs to control the camera to do some special motion, and the internal parameters of the camera can be calculated by using the particularity of this motion. The advantage of the camera calibration method based on active vision is that the algorithm is simple and can often obtain a linear solution, so it has high robustness. Uncontrollable occasions.
