Nowadays, binocular stereo vision (BSV) is extensively used in real-time 3D reconstruction, which requires cameras to quickly implement self-calibration. At present, the camera parameters are typically estimated through iterative optimization. The calibration accuracy is high, but the process is time consuming. Hence, a system of BSV with rotating and non-zooming cameras is established in this study, in which the cameras can rotate horizontally and vertically. The cameras’ intrinsic parameters and initial position are estimated in advance by using Zhang’s calibration method. Only the yaw rotation angle in the horizontal direction and pitch in the vertical direction for each camera should be obtained during rotation. Therefore, we present a novel self-calibration method by using a single feature point and transform the imaging model of the pitch and yaw into a quadratic equation of the tangent value of the pitch. The closed-form solutions of the pitch and yaw can be obtained with known approximate values, which avoid the iterative convergence problem. Computer simulation and physical experiments prove the feasibility of the proposed method. Additionally, we compare the proposed method with Zhang’s method. Our experimental data indicate that the averages of the absolute errors of the Euler angles and translation vectors relative to the reference values are less than 0.21° and 6.6 mm, respectively, and the averages of the relative errors of 3D reconstruction coordinates do not exceed 4.2%.

In this paper, a novel 3D gaze estimation method for a wearable gaze tracking device is proposed. This method is based on the pupillary accommodation reflex of human vision. Firstly, a 3D gaze measurement model is built. By uniting the line-of-sight convergence point and the size of the pupil, this model can be used to measure the 3D Point-of-Regard in free space. Secondly, a gaze tracking device is described. By using four cameras and semi-transparent mirrors, the gaze tracking device can accurately extract the spatial coordinates of the pupil and eye corner of the human eye from images. Thirdly, a simple calibration process of the measuring system is proposed. This method can be sketched as follows: (1) each eye is imaged by a pair of binocular stereo cameras, and the setting of semi-transparent mirrors can support a better field of view; (2) the spatial coordinates of the pupil center and the inner corner of the eye in the images of the stereo cameras are extracted, and the pupil size is calculated with the features of the gaze estimation method; (3) the pupil size and the line-of-sight convergence point when watching the calibration target at different distances are computed, and the parameters of the gaze estimation model are determined. Fourthly, an algorithm for searching the line-of-sight convergence point is proposed, and the 3D Point-of-Regard is estimated by using the obtained line-of-sight measurement model. Three groups of experiments were conducted to prove the effectiveness of the proposed method. This approach enables people to obtain the spatial coordinates of the Point-of-Regard in free space, which has great potential in the application of wearable devices.

This paper proposes a 3D point-of-regard estimation method based on 3D eye model and a corresponding head-mounted gaze tracking device. Firstly, a head-mounted gaze tracking system is given. The gaze tracking device uses two pairs of stereo cameras to capture the left and right eye images, respectively, and then sets a pair of scene cameras to capture the scene images. Secondly, a 3D eye model and the calibration process are established. Common eye features are used to estimate the eye model parameters. Thirdly, a 3D point-of-regard estimation algorithm is proposed. Three main parts of this method are summarized as follows: (1) the spatial coordinates of the eye features are directly calculated by using stereo cameras; (2) the pupil center normal is used to the initial value for the estimation of optical axis; (3) a pair of scene cameras are used to solve the actual position of the objects being watched in the calibration process, and the calibration for the proposed eye model does not need the assistance of the light source. Experimental results show that the proposed method can output the coordinates of 3D point-of-regard more accurately.

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