Estimating 3-D Point-of-Regard in a Real Environment Using a Head-Mounted Eye-Tracking System

Takemura, Kentaro; Takahashi, Kenji; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/thms.2014.2318324

Cited by 50 publications

(26 citation statements)

References 19 publications

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“…Takemura et al [2] proposed a 3D PoR estimation method combining scene images. The pupil vector is used as the initial value of gaze direction, then corner feature extraction and Delaunay triangulation are used to analyze scene images then estimate possible PoR.…”

Section: Related Workmentioning

confidence: 99%

“…The head-mounted gaze tracking device is a potential human-computer interface system in free space. However, the accurate estimation of the 3D PoR on classical methods still needs the assistance of scene information and inertial sensors [2]. This research also focuses on the 3D PoR estimation method based on a head-mounted gaze tracking system.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

et al. 2018

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

et al. 2018

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“…The traditional HMD technology also has improved. Takemura, K. et al [17] reported their work on catching the person's gaze which can effectively improve the user experience.…”

Section: Presenting Composite Datamentioning

confidence: 99%

Seam the Real with the Virtual:a Review of Augmented Reality

Zheng¹,

Zhang²,

Yang³

2015

Proceedings of the 2015 Information Technology and Mechatronics Engineering Conference

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Abstract. In this paper, we reviewed several outstanding contributions over the past decade in the field of augmented reality. The papers are selected according to their citation rate in the academic resources of Google scholar. In addition, considering that augmented reality covers several topics, and it has intersection with pervasive computing and wearable computing in concept, only the promising technologies and some prominent cases are concerned. In our opinion, a typical AR application is a solution for special application scenarios, and in most cases a comprehensive system is needed. For simplicity, we divided the technologies adopted in AR application into three categories: acquiring the data of environment, registering the virtual object in real environment and presenting the composite data. At the end of the paper, we discussed the remained problems and future development trend of augmented reality.

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“…In prior work, 3D gaze estimation was approximately addressed as a projection from estimated 2D gaze positions in the scene camera image to the corresponding 3D scene [Munn and Pelz 2008;Takemura et al 2014;Pfeiffer and Renner 2014]. However, without proper 3D gaze estimation, gaze mapping suffers from parallax error caused by the offset between the scene camera origin and eyeball position [Mardanbegi and Hansen 2012;Duchowski et al 2014].…”

Section: Introductionmentioning

confidence: 99%

3D gaze estimation from 2D pupil positions on monocular head-mounted eye trackers

Mansouryar

Steil

Sugano

et al. 2016

Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research &Amp; Applications

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Abstract3D gaze information is important for scene-centric attention analysis, but accurate estimation and analysis of 3D gaze in real-world environments remains challenging. We present a novel 3D gaze estimation method for monocular head-mounted eye trackers. In contrast to previous work, our method does not aim to infer 3D eyeball poses, but directly maps 2D pupil positions to 3D gaze directions in scene camera coordinate space. We first provide a detailed discussion of the 3D gaze estimation task and summarize different methods, including our own. We then evaluate the performance of different 3D gaze estimation approaches using both simulated and real data. Through experimental validation, we demonstrate the effectiveness of our method in reducing parallax error, and we identify research challenges for the design of 3D calibration procedures.

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Estimating 3-D Point-of-Regard in a Real Environment Using a Head-Mounted Eye-Tracking System

Cited by 50 publications

References 19 publications

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision

Seam the Real with the Virtual:a Review of Augmented Reality

3D gaze estimation from 2D pupil positions on monocular head-mounted eye trackers

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