Camera Calibration Method Research Articles

A geometric calibration method for thermal cameras using a ChArUco board

Accurate and efficient geometric calibration is important to precisely acquire geometric information in thermal cameras. Typically, the calibration procedure involves obtaining defined control points from the images of a calibration target and utilizing them to estimate the geometric calibration parameters based on a camera model. Calibration targets include checkerboards, circle grids, Hermann grids, and specially designed patterns. Thermal cameras typically have lower resolution than visible cameras and the control points are localized by creating contrast through heating. These differences make the calibration process of thermal cameras more complex. Conventional thermal camera calibration methods require that the entire calibration target is captured and that all the control points from each image are identified. Due to these limitations, geometric calibration of a thermal camera traditionally requires advanced image processing or substantial manual intervention.This work presents a calibration method using a ChArUco board as the calibration target. The proposed method allows the calibration to be performed with partial-view images and does not require that all the control points from each image are extracted, thereby allowing the calibration to be performed without requiring complex image processing. The proposed calibration method was successfully implemented to estimate the calibration parameters of two different thermal cameras; in both cases achieving an overall mean reprojection error below 0.4 pixels. The effectiveness of the approach was further demonstrated by performing the calibration with a lower mean reprojection error than the MATLAB camera calibrator app. Furthermore, the effectiveness of using different heat sources to create contrast was evaluated.

A deep-learning based high-accuracy camera calibration method for large-scale scene

Accurate three-dimensional (3D) measurement for large field of view (FOV) is currently a significant research field. Accordingly, system calibration is crucial to ensure accuracy. However typical calibration methods often involve the use of large calibration objects, which is not only expensive but also difficult to achieve sufficient accuracy. A novel method based on a dual-brand deep neural network (DNN) is proposed for the system calibration. Taking advantage of the concept of “divide and conquer”, the FOV is divided into sub-regions with a part of overlapping regions by a small calibration object, which forms a large calibration object covering the whole FOV. Then the sub-regions are fused into a global framework and further optimized by the proposed dual-brand DNN. The proposed method reduces the need for calibration objects while improving the calibration accuracy and generalization ability in large FOV. A series of experiments have been designed to prove the effectiveness and robustness of the proposed method.

A multi‐degree‐of‐freedom monitoring method for slope displacement based on stereo vision

AbstractThree‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Iterative Camera Calibration Method Based on Concentric Circle Grids

A concentric circle target is commonly used in the vision measurement system for its detection accuracy and robustness. To enhance the camera calibration accuracy, this paper proposes an improved calibration method that utilizes concentric circle grids as the calibration target. The method involves accurately locating the imaged center and optimizing camera parameters. The imaged concentric circle center obtained by cross-ratio invariance is not affected by perspective projection, which ensures the location accuracy of the feature point. Subsequently, the impact of lens distortion on camera calibration is comprehensively investigated. The sub-pixel coordinates of imaged centers are taken into the iterative calibration method, and camera parameters are updated. Through simulations and real experiments, the proposed method effectively reduces the residual error and improves the accuracy of camera parameters.

Adaptive Point-Line Fusion: A Targetless LiDAR-Camera Calibration Method with Scheme Selection for Autonomous Driving.

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR-camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

A Target-based co-calibration framework for 3DRadar-camera using a modified corner reflector

Most intelligent transportation and autonomous driving systems use the combination of millimeter-wave (MMW) radar and camera to achieve strong perception, and correct extrinsic calibration is a prerequisite for sensor fusion. Most prior calibration methods rely on specific calibration environment, resulting in low calibration efficiency, and are unsuitable for practical scenarios. This paper proposes a progressive and efficient extrinsic calibration method for 3D MMW radar and camera, which only requires testers to walk around the testing range with the calibration target, and the progressive process of extrinsic parameters regression is visualized. The proposed method has been tested on the View-of-Delft dataset and in the real world, and the results show that the method proposed in this paper can converge the calibrated extrinsic parameters quickly and has strong robustness to the noise present during the testing process. Compared to EPNP, LM, P3P (based on RANSAC), and LM (based on RANAC), our proposed calibration method demonstrates a smaller re-projection error and higher accuracy in terms of extrinsic parameters. All results indicate that our calibration method has good accuracy and efficiency for practical calibration scenarios.

In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.

Camera optimal projection model identification and calibration method based on the NGO-BA architecture.

In order to bridge the fundamental commonalities between imaging models of camera lenses with different principles and structures, allowing for an accurate description of imaging characteristics across a wide range of field-of-view (FOV), we have proposed a generic camera calibration method on the basis of the projection model optimization strategy. First, in order to cover the current mainstream projection models, piecewise functions for geometric projection models and a polynomial function for the fitting projection model are designed. Then, the corresponding camera multistation self-calibration bundle adjustment (BA) module is developed for various projection models. Further, by integrating the self-calibration BA algorithm into the northern goshawk optimization architecture, iterative optimization is performed on the projection model adjustment parameters, camera interior parameters, camera exterior parameters, and lens distortion parameters until the target reprojection (RP) error reaches the global minimum. The experimental results indicate that the calibration RP root mean square error in this method is 1/20 pixel for a 68° FOV camera, 1/13 pixel for an 84° FOV camera, 1/9 pixel for a 115° FOV camera, 1/9 pixel for a 135° FOV camera, and 1/6 pixel for a 180° FOV camera. This calibration method offers fast and versatile optimization for various projection model types, encompassing a wide range of projection functions. It can efficiently determine the optimal projection model and all imaging parameters for multiple cameras during the calibration process.

Laboratory Geometric Calibration Method for Multi-band Fisheye Lens Camera

Laboratory Geometric Calibration Method for Multi-band Fisheye Lens Camera

Calibration method of fisheye camera for high-precision collimation measurement

Calibration method of fisheye camera for high-precision collimation measurement

Polarizing Camera Array System Equipment and Calibration Method

Polarizing Camera Array System Equipment and Calibration Method

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

Line-Scan Camera Calibration Method Based on Absolute Phase Target

Line-Scan Camera Calibration Method Based on Absolute Phase Target

ANALYSIS OF CALIBRATION ERRORS IN AUTOMOBILE CONTROL TELEVISION SYSTEM

Camera calibration is a critical method for 3D measurement applications based on 2D images. During calibration, internal camera parameters such as principal point and focal length are determined. The principal point is the intersection of the camera's optical axis and the image plane, while the focal length is measured from the lens center to the principal point.This article theoretically investigates calibration errors in two reference planes considering various conditions, such as shifts in the reference plane and changes in focal lengths. Errors in determining the coordinates of reference points in images were used to evaluate these calibration errors.The error in estimating the focal length and principal point is formulated using analytical equations, and the results are compared with simulation outcomes to validate the effectiveness of the proposed formulas. The research reveals that calibration errors arise due to shifts in the reference plane and changes in focal lengths. The obtained results are crucial for enhancing camera calibration methods and improving the accuracy of 3D measurements based on 2D images.This article extends the understanding of camera calibration errors and provides new theoretical approaches for their assessment. The research findings may be beneficial for scientists and practitioners working in the fields of computer vision, image processing, and 3D measurements.Further research in this area may contribute to the development of new calibration methods and approaches that will improve the performance of automotive systems and ensure high accuracy and reliability in the future.

Equivalent distortion imaging model-based calibration method for a projection moiré system using combined lenses

The projection moiré method is a noncontact full-field optical topography measurement technique, which requires a precise model and corresponding calibration method; however, the existing models and calibration methods are either incompatible with system distortion or involve complex experimental operations. This study presents an equivalent distortion imaging model coupled with a flexible calibration method that overcomes such shortcomings. The proposed model is constructed by considering the stacking effect of the distortions of the two lenses and the misalignment between the optical axes. The corresponding calibration method leverages the existing camera calibration method to avoid complex operations. The proposed model has high precision owing to its ability to describe the complex imaging of the system. Moreover, the calibration is flexible and requires far fewer operations than with the two-stage calibration method. Both simulations and experiments verify the effectiveness of the proposed method.

Camera calibration method through multivariate quadratic regression for depth estimation on a stereo vision system

Machine vision systems have demonstrated practical applications in various fields where the perception of the environment is essential. Stereo vision systems are one of the most used methods to perform three-dimensional mapping. These systems have several considerations in order to fulfill this task; One of them is camera calibration. This is why this paper proposes a novel camera calibration method for improving depth estimation accuracy in stereo vision systems. The method addresses the lens distortion issue by adjusting the surface point's pixel coordinates before the triangulation process. The new pixel coordinates are computed using calibration coefficients, which are calculated through multivariate quadratic regression. Also, the proposed method corrects the relative orientation of the cameras and computes a compensation angle. The proposed method is remarkably robust to varying illumination levels because it adjusts pixel positions independently of image content. In comparison to traditional methods, this method requires fewer steps to implement, as it only requires one image per camera. The proposed method could have significant implications for fields such as autonomous navigation and robotics, where depth estimation is a crucial component. Overall, this paper presents a valuable contribution to the field of computer vision by offering a new, efficient and effective approach to camera calibration for stereo vision systems. The performed experiments demonstrated that the proposed method leads to improved depth estimation in the stereo vision system with an improvement of 34.15% on the MAE and 48.38% on the STD when compared to one of the most commonly used methods.

A novel camera calibration method based on circle projection model

Camera calibration is one of the important factors affecting measurement accuracy in the field of visual measurement, and the camera calibration method with high precision and strong robustness is the prerequisite for ensuring high-precision visual measurement. Among the commonly used calibration methods, target-based calibration methods have high accuracy and good flexibility. However, the feature extraction of checkerboard targets is prone to noise interference. Circle targets have higher feature extraction accuracy, but will introduce eccentricity errors in the center of the circle projection. In order to eliminate the eccentricity error of circle targets, this paper proposes a camera calibration method based on the circle projection model, which does not rely on the central projection coordinates, but directly constructs the mapping between the circle equation on the target and the elliptic equation in the camera image, and solves the optimal mapping parameters through nonlinear optimization algorithm. Through simulation and experiments, it has been proven that the calibration method based on the circle projection model has high calibration accuracy and anti-noise interference ability, providing a guarantee for high-precision visual measurement.

A Systematic Stereo Camera Calibration Strategy: Leveraging Latin Hypercube Sampling and 2k Full-Factorial Design of Experiment Methods.

This research aimed to optimize the camera calibration process by identifying the optimal distance and angle for capturing checkered board images, with a specific focus on understanding the factors that influence the reprojection error (ϵRP). The objective was to improve calibration efficiency by exploring the impacts of distance and orientation factors and the feasibility of independently manipulating these factors. The study employed Zhang's camera calibration method, along with the 2k full-factorial analysis method and the Latin Hypercube Sampling (LHS) method, to identify the optimal calibration parameters. Three calibration methods were devised: calibration with distance factors (D, H, V), orientation factors (R, P, Y), and the combined two influential factors from both sets of factors. The calibration study was carried out with three different stereo cameras. The results indicate that D is the most influential factor, while H and V are nearly equally influential for method A; P and R are the two most influential orientation factors for method B. Compared to Zhang's method alone, on average, methods A, B, and C reduce ϵRP by 25%, 24%, and 34%, respectively. However, method C requires about 10% more calibration images than methods A and B combined. For applications where lower value of ϵRP is required, method C is recommended. This study provides valuable insights into the factors affecting ϵRP in calibration processes. The proposed methods can be used to improve the calibration accuracy for stereo cameras for the applications in object detection and ranging. The findings expand our understanding of camera calibration, particularly the influence of distance and orientation factors, making significant contributions to camera calibration procedures.

A Binocular Camera Calibration Method in Froth Flotation Based on Key Frame Sequences and Weighted Normalized Tilt Difference

3D froth information is a significant indicator of working condition recognition in froth flotation. To extract it accurately, the first and key step is camera calibration. Although there have been many camera calibration methods proposed in these years, they cannot handle well for binocular camera calibration in froth flotation. The main reason is that there is an open environment without support surface or special geometric shapes for camera calibration in froth flotation. Therefore, we propose a novel binocular camera calibration method based on key frame sequences and weighted normalized tilt difference. First, we extract a high-quality frame sequence by filtering out incomplete and out-of-focus images from the froth video. Then, we propose a method to measure the tilt degree of the planar plate and extract the key frame sequences of the planar plate based on the tilt difference. After that, we calculate the camera parameters by weighted normalized tilt difference under various poses. The proposed binocular camera calibration method uses the froth video to obtain accurate and robust camera parameters, and does not require auxiliary equipment. Experiments have demonstrated the effectiveness and flexibility of the proposed method in froth flotation.