Estimating 3D point-of-regard and visualizing gaze trajectories under natural head movements

Takemura, Kentaro; Kohashi, Yuji; Suenaga, Tsuyoshi; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1145/1743666.1743705

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Basically, there are two approaches. The first one, shown in Figure , is to visualize scanpaths in the 3D domain of the stimulus [DMC*02, SND10b, TKS*10, Pfe12, PSF*13a]. The other one is to warp the stimulus into a 2D representation and to draw scanpath lines on this 2D image [RTSB04].…”

Section: Point‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

223

115

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This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

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Section: Point‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

223

115

View full text Add to dashboard Cite

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“…Figure 2 shows the process of estimating a 3D POR. The process generates a triangular mesh of the PTAM points that are visible on the current frame [5,31]. The 3D POR X is calculated from the triangle X 0 X 1 X 2 of visible PTAM points Session: EyeWear Computing ISWC'13, September 9-12, 2013, Zurich, Switzerland which includes the intersection point…”

Section: D Point-of-regardsmentioning

confidence: 99%

“…This allows for triangulating its position in the world but this technique is clearly cumbersome when multiple points of interest are needed. This problem has been recently addressed by employing visual mapping (such as SLAM) techniques to estimate the user's movement and obtain directly 3D PORs [4,31].…”

Section: Introductionmentioning

confidence: 99%

3D from looking

Leelasawassuk

Mayol-Cuevas

2013

Proceedings of the 2013 International Symposium on Wearable Computers

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This paper presents a method for estimating the 3D shape of an object being observed using wearable gaze tracking. Starting from a sparse environment map generated by a simultaneous localization and mapping algorithm (SLAM), we use the gaze direction positioned in 3D to extract the model of the object under observation. By letting the user look at the object of interest, and without any feedback, the method determines 3D point-of-regards by back-projecting the user's gaze rays into the map. The 3D point-of-regards are then used as seed points for segmenting the object from captured images and the calculated silhouettes are used to estimate the 3D shape of the object. We explore methods to remove outlier gaze points that result from the user saccading to non object points and methods for reducing the error in the shape estimation. Being able to exploit gaze information in this way, enables the user of wearable gaze trackers to be able to do things as complex as object modelling in a hands-free and even feedback-free manner.

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“…The 3D PoG can be obtained easily from the 2D point by looking up the 3D coordinates of the pixel in the point cloud data struc ture provided by the RGB-D camera. Exploitation of the RGB-D point cloud structure removes the need for stereo eye tracking during 3D PoG estimation as used in [4,5].…”

Section: Point Of Gaze Estimationmentioning

confidence: 99%

3D point of gaze estimation using head-mounted RGB-D cameras

McMurrough

Conly

Athitsos

et al. 2012

Proceedings of the 14th International ACM SIGACCESS Conference on Computers and Accessibility

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This paper presents a low-cost, wearable headset for 3D Point of Gaze (PoG) estimation in assistive applications. The device consists of an eye tracking camera and forward facing RGB-D scene camera which, together, provide an es timate of the user gaze vector and its intersection with a 3D point in space. The resulting system is able to compute the 3D PoG in real-time using inexpensive and readily available hardware components.

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Estimating 3D point-of-regard and visualizing gaze trajectories under natural head movements

Cited by 17 publications

References 10 publications

Visualization of Eye Tracking Data: A Taxonomy and Survey

Visualization of Eye Tracking Data: A Taxonomy and Survey

3D from looking

3D point of gaze estimation using head-mounted RGB-D cameras

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