A Simplified 3D Gaze Tracking Technology with Stereo Vision

Zhang, Ke; Zhao, Xinbo; Ma, Zhengliang; Man, Yi

doi:10.1109/icoip.2010.346

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…1. This finding is based on recent research on human stereo vision [24]. These effects cause viewers to feel immersed in the image, which is the most attractive aspect of stereoscopic images.…”

Section: Methodsmentioning

confidence: 99%

Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

Cheng

Zhang

et al. 2017

J Image Video Proc.

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In this paper, we exploit two characteristics of stereoscopic vision: the pop-out effect and the comfort zone. We propose a visual saliency prediction model for stereoscopic images based on stereo contrast and stereo focus models. The stereo contrast model measures stereo saliency based on the color/depth contrast and the pop-out effect. The stereo focus model describes the degree of focus based on monocular focus and the comfort zone. After obtaining the values of the stereo contrast and stereo focus models in parallel, an enhancement based on clustering is performed on both values. We then apply a multi-scale fusion to form the respective maps of the two models. Last, we use a Bayesian integration scheme to integrate the two maps (the stereo contrast and stereo focus maps) into the stereo saliency map. Experimental results on two eye-tracking databases show that our proposed method outperforms the state-of-the-art saliency models.

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Section: Methodsmentioning

confidence: 99%

Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

Cheng

Zhang

et al. 2017

J Image Video Proc.

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“…Some researchers rely on non-contact eye image acquisition equipment for line-ofsight tracking research. Zhang et al [18] developed a simplified 3D line-of-sight tracking technology with stereo vision. Compared with other 3D systems, this paper uses the first image of two LEDs to estimate the 3D corneal center and pupil center.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking

Zhang

et al. 2023

Electronics

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Aiming at the problem of feature point tracking loss caused by large head rotation and facial occlusion in doctors, this paper designs a head-position-tracking system based on geometric triangles and unscented Kalman filtering. By interconnecting the three feature points of the left and right pupil centers and the tip of the nose, they form a coplanar triangle. When the posture of the doctor’s head changes due to rotation, the shape of the corresponding geometric triangle will also deform. Using the inherent laws therein, the head posture can be estimated based on changes in the geometric model. Due to the inaccurate positioning of feature points caused by the deflection of the human head wearing a mask, traditional linear Kalman filtering algorithms are difficult to accurately track feature points. This paper combines geometric triangles with an unscented Kalman Filter (UKF) to obtain head posture, which has been fully tested in different environments, such as different faces, wearing/not wearing masks, and dark/bright light via public and measured datasets. The final experimental results show that compared to the linear Kalman filtering algorithm with a single feature point, the traceless Kalman filtering algorithm combined with geometric triangles in this paper not only improves the robustness of nonlinear angle of view tracking but also can provide more accurate estimates than traditional Kalman filters.

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“…Other systems, such as [24] and [25], make use of multiple cameras as a stereo camera configuration to use a 3D model of the eye to estimate eye gaze.…”

Section: Multiple Camera and M Ultiple Light Source Systemsmentioning

confidence: 99%

“…The method reduced the average root mean square error of the estimated x-coordinate by 64.5 + /-39.3 pixels (16.7 + /-10.2mm) and the estimated y-coordinate by 53.1 + /-14.7 pixels (13.7 + /-3.8mm) on the monitor screen. This translates to a reduction of 28.5% + /-17.2% for the x-coordinate and 25.7% + /-6.6% for the y-coordinate of the eye gaze estimate. Current system limitations are also discussed that detail possible future work.…”

Section: Introductionmentioning

confidence: 97%

A non-intrusive and calibration-free gaze tracking system

Luong¹

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A Simplified 3D Gaze Tracking Technology with Stereo Vision

Cited by 11 publications

References 6 publications

Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

Stereoscopic visual saliency prediction based on stereo contrast and stereo focus

An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking

A non-intrusive and calibration-free gaze tracking system

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