This study proposes an automatic reading approach for a pointer gauge based on computer vision. Moreover, the study aims to highlight the defects of the current automatic-recognition method of the pointer gauge and introduces a method that uses a coarse-to-fine scheme and has superior performance in the accuracy and stability of its reading identification. First, it uses the region growing method to locate the dial region and its center. Second, it uses an improved central projection method to determine the circular scale region under the polar coordinate system and detect the scale marks. Then, the border detection is implemented in the dial image, and the Hough transform method is used to obtain the pointer direction by means of pointer contour fitting. Finally, the reading of the gauge is obtained by comparing the location of the pointer with the scale marks. The experimental results demonstrate the effectiveness of the proposed approach. This approach is applicable for reading gauges whose scale marks are either evenly or unevenly distributed.

Traditional 3D gaze estimation methods are usually based on the models of pupil refraction and corneal reflection. These methods typically rely on multiple light sources. The 3D gaze can be estimated using single-camera-single-light-source systems only when certain user-dependent eye parameters are available a priori, which is rarely the case. This article proposes a 3D gaze estimation method which works based on iris features using a single camera and a single light source. User-dependent eye parameters involving the iris radius and the cornea radius are user-calibrated. The 3D line-of-sight is estimated from the optical axis and the positional relationship between the optical axis and the visual axis, and then optimized using a binocular stereo vision model. The feasibility and robustness of the proposed method are assessed by simulations and practical experiments. The system configuration required by the method is simpler than that required by the state-of-the-art methods, which shows significant potential value, especially with regard to mobile device applications.

Multi-camera or multi-light-source is generally used to estimate the 3D coordinates of the cornea center for 3D gaze estimation in the existing gaze trackers. Although some model-based methods use one-camera systems to estimate 3D gazes, which include some user-dependent eye parameters preset by fixed values, a one-camera-one-light-source system is still unable to achieve 3D gaze estimation by taking individual differences into consideration. In this paper, an iris feature based method using one camera and one light source for 3D gaze estimation is proposed. The iris radius and the kappa angle are determined during user calibration. The optical axis is reconstructed by the iris center and its normal vector from only the iris features. Therefore, with the real-time estimation of kappa angle, the 3D gaze is estimated by an optimization method. Computer simulations and practical experiments have been performed to analyze the feasibility and robustness of proposed method. This paper’s innovation lies in achieving 3D gaze estimation based on only one camera and one light source with calibrated eye-specific parameters, which breaks through the limitation of the existing methods using a 3D cornea center on the system’s hardware requirements of 3D gaze estimation. The simplified hardware system has great application values, especially in the portable mobile devices widely used today. Moreover, a binocular model is used to optimize the results of the point-of-regard, which improves the accuracy of 3D gaze estimation effectively.

The image of the center of a 3D circular target is not the center of its imaging ellipse due to the imaging distortion of spatial circular targets, therefore, traditional 3D gaze estimation methods with multi-camera-multi-light-source systems generally replace the projection of the pupil center with the virtual image of the pupil center for 3D gaze estimation. However, this introduces a large gaze estimation error when the oblique angle between the optical axis of the eye and the camera optical axis is large. To eliminate the error caused by using the virtual image of the pupil center, a 3D gaze estimation method using a multi-camera-multilight-source system is developed in this study. We first estimate the cornea center, and then determine the matching points of the pupil imaging ellipse using the special polar plane. After the cornea radius and the kappa angle are calibrated, the optical axis of the eye is reconstructed by refraction planes constructed by the edge points of pupil imaging ellipses. Thus the 3D gaze is estimated by the real-time calculated transformation matrix represented the kappa angle. The feasibility and performance of the method has been analyzed via simulations and experiments. Since the estimation of spatial pupil center or the construction of refraction plane using the virtual image of the pupil center is not required, the proposed method mitigates the inherent errors caused by optical axis reconstruction in the traditional methods and simplifies the algorithm for gaze estimation, which has practical value.

In a 3D gaze tracking system, system-calibration is used to determine the positions of the light sources and the screen in camera coordinate system, which plays an important role in the estimation of the 3D gaze and the point of regard (PoR). The traditional way for the system-calibration is the mirror-based method, in which, a mirror is used to make the light sources and the screen can be captured by the camera through specular reflection. However, the mirror-based method is imprecise and complex. In this paper, a novel system-calibration method for the 3D gaze tracking system is proposed. On the basis of the system camera, a calibration camera is placed on the opposite of the screen to constitute a multi-camera system. Images are captured by the two cameras to estimate the light sources and the screen of the system camera coordinate frame. Simulations and experiments were carried out to illustrate the validation of the method. This method solves the problem that the light sources and the screen are not in view of the system camera. In addition, it is easy to implement while ensuring high accuracy and providing a new method for the other similar calibration systems.

Personal calibration is a process of obtaining personal gaze-related information by focusing on some calibration benchmarks when the user initially uses a gaze tracking system. It not only provides conditions for gaze estimation, but also improves gaze tracking performance. Existing eye-tracking products often require users to conduct explicit personal calibration first, thereby tracking and interacting based on their gaze. This calibration mode has certain limitations, and there is still a significant gap between theoretical personal calibration methods and their practicality. Therefore, this paper reviews the issues of personal calibration for video-oculographic-based gaze tracking. The personal calibration information in typical gaze tracking methods is first summarized, and then some main settings in existing personal calibration processes are analyzed. Several personal calibration modes are discussed and compared subsequently. The performance of typical personal calibration methods for 2D and 3D gaze tracking is quantitatively compared through simulation experiments, highlighting the characteristics of different personal calibration settings. On this basis, we discuss several key issues in designing personal calibration. To the best of our knowledge, this is the first review on personal calibration issues for video-oculographic-based gaze tracking. It aims to provide a comprehensive overview of the research status of personal calibration, explore its main directions for further studies, and provide guidance for seeking personal calibration modes that conform to natural human-computer interaction and promoting the widespread application of eye-movement interaction.