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WO1999026126A1 - Interface utilisateur - Google Patents

Interface utilisateur Download PDF

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Publication number
WO1999026126A1
WO1999026126A1 PCT/GB1998/003441 GB9803441W WO9926126A1 WO 1999026126 A1 WO1999026126 A1 WO 1999026126A1 GB 9803441 W GB9803441 W GB 9803441W WO 9926126 A1 WO9926126 A1 WO 9926126A1
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WO
WIPO (PCT)
Prior art keywords
user
gaze
training
neural network
eye
Prior art date
Application number
PCT/GB1998/003441
Other languages
English (en)
Inventor
Behnam Azvine
David Djian
Kwok Ching Tsui
Li-Qun Xu
Original Assignee
British Telecommunications Public Limited Company
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GBGB9724277.0A external-priority patent/GB9724277D0/en
Application filed by British Telecommunications Public Limited Company filed Critical British Telecommunications Public Limited Company
Priority to AU11657/99A priority Critical patent/AU1165799A/en
Priority to EP98954602A priority patent/EP1032872A1/fr
Publication of WO1999026126A1 publication Critical patent/WO1999026126A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/013—Eye tracking input arrangements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/113—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for determining or recording eye movement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G06F18/2414—Smoothing the distance, e.g. radial basis function networks [RBFN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/18—Eye characteristics, e.g. of the iris
    • G06V40/193—Preprocessing; Feature extraction

Definitions

  • IPC International Patent Classification
  • WOLFE B ET AL "A neural network approach 1,13,15, to tracking eye position"
  • P0MPLUN M ET AL "An artificial neural 1,13,15, network for high precision eye movement 16 tracking"
  • the present invention relates to a user interface for a data or other software system, which monitors an eye of the user, such as a gaze tracker.
  • the interface finds particular but not exclusive application in a multimodal system.
  • Gaze tracking is a challenging and interesting task traversing several disciplines including machine vision, cognitive science and human computer interactions (Velichkovsky, B.M. and J.P. Hansen ( 1 996): "New technological windows into mind: There is more in eyes and brains for human-computer interaction". Technical Report: Unit of Applied Cognitive Research, Dresden University of Technology, Germany).
  • the idea that a human subject's attention and interest on a certain object, reflected implicitly by eye movements, can be captured and learned by a machine which can then act automatically on the subject's behalf lends itself to many applications, including for instance video conferencing (Yang, J., L. Wu and A.
  • Waibel (1996): “Focus of attention in video conferencing”: Technical Report, CMU-CS-96-150, School of Computer Science, Carnegie Mellon University, June 1 996).
  • This idea can be used for instance for: • focusing on interesting objects and transmitting selected images of them through the communication networks,
  • gaze tracking uses the so-called pupil-center/corneal- reflection method (Cleveland, D. and N. Cleveland (1 992)- "Eyegaze eyetracking system" Proc. of 1 1 th Monte-Carlo International Forum on New Images, Monte- Carlo, January 1 992).
  • This uses controlled infra-red lighting to illuminate the eye, computing the distance between the pupil centre (the bright-eye effect) and the small very bright reflection off the surface of the eye's cornea to find the line of sight on the display screen, through geometric projections.
  • This kind of method normally involves a specialised high speed/high resolution camera, a controlled lighting source and electronic hardware equipment, and is sometimes intrusive (Stampe, D.
  • a gaze tracker is described in US-A-5 481 622, which comprises a helmet worn by a user, which uses a camera to acquire a video image of the pupil, mounted on the helmet.
  • a frame grabber is coupled to the camera to accept and convert analog data from the camera into digital pixel data.
  • a computer coupled to the frame grabber processes the digital pixel data to determine the position of the pupil.
  • a display screen is coupled to the computer and is mounted on the helmet. The system is calibrated by the user following a cursor on the display screen while the system measures the pupil position for known locations of the cursor.
  • the arrangement is complicated and requires special hardware namely the helmet arrangement and is not suited to everyday commercial use.
  • US-A-5 471 542 discloses a gaze tracker in which a video camera is provided on a personal computer in order to detect eye movement to perform functions similar to those achieved with a conventional hand-held mouse.
  • a user interface for use in making inputs to a data or communications system, responsive to the user's eye, comprising: i) a scanning device for capturing a quantised image of an eye; ii) a pupil image detector to detect a representation of the pupil of the eye in the quantised image; iii) a display for a plurality of visual targets; iv) a first learning device to relate at least one variable characteristic of said image of the eye to a selected one of said visual targets; and v) a second learning device, for relating external parameters apparent to a user of the system to parameters internal to the system.
  • the invention also provides in another aspect a method of training the user interface, which involves displaying training data on the display and training the first learning device to relate the variable characteristic of the image of the eye to the training data when the user gazes at the displayed training data.
  • the invention may also include training the second learning device to relate external parameters apparent to the user of the system to the internal parameters.
  • the internal parameters may be a function of fixation of the gaze of the user at a particular region on the display, and the external parameters may include the time taken to determine that a fixation has occurred and the positional accuracy thereof.
  • Embodiments of the present invention provide a real-time non-intrusive gaze tracking system; that is, a system which can tell where a user is looking, for instance on a computer screen.
  • the gaze tracking system can provide a vision component of a multimodal intelligent interface, particularly suited for resolving ambiguities and tracking contextual dialogue information.
  • it is also an effective piece of technology in its own right, leading to many potential applications in human-computer interactions where the ability to find human attention is of significant interest.
  • the second learning device may be provided to relate parameters apparent to a user of the system to parameters internal to the system. This can be used to provide an adjustment capability such that the user can adjust parameters apparent in use of the system by inputting parameter information to the system, the system responding thereto by adjusting parameters internal to the system, in accordance with one or more learned relationships therebetween.
  • the invention provides a gaze tracker including means for determining when a user achieves a gaze fixation on a target, comprising learning means for learning a relationship between response time and accuracy for achieving a fixation, and means responsive to a user's preference concerning the relationship for controlling signification of the fixation.
  • the learning means may comprise a Bayesian net.
  • the invention also includes a user interface for a computer workstation usable for videoconferencing, the interface being configured for use in making inputs to a data or communications system in response to movements of the user's eye, comprising: i) a tv videoconferencing camera to be mounted on the workstation for capturing a quantised image of an eye; ii) a pupil image detector to detect a representation of the pupil of the eye in the quantised image; iii) a workstation display for a plurality of visual targets; and a neural net to relate at least one variable characteristic of said image of the eye to a selected one of said visual targets.
  • Figure 1 shows in schematic outline a neural network based gaze modelling/tracking system as an embodiment of the present invention wherein Figure 1 A illustrates the physical configuration and Figure 1 B illustrates the system in terms of functional blocks;
  • Figure 2 shows a snapshot of a captured image of a user's head image for use in the system shown in Figure 1 ;
  • Figure 3 shows an example of a fully segmented eye image for use in the system shown in Figure 1 ;
  • Figure 4 shows a histogram of a segmented grayscale eye image
  • Figure 5 shows a transfer function for the normalisation of segmented eye image data
  • Figure 6 shows a normalised histogram version of the eye image of Figure 3;
  • Figure 8 shows a matrix of grids laid over a display screen for the collection of training data for use in a system according to Figure !;
  • Figure 9 shows a Gaussian shaped output activation pattern corresponding to the vertical position of a gaze point
  • Figure 10 shows training errors versus number of training epochs in a typical training trial of the network shown in Figure 7;
  • Figure 1 1 shows learning and validation errors versus number of training epochs in a trial of the learning process for the network shown in Figure 7;
  • Figure 12 shows a histogram of the neural network's connection weights after 100 training epochs.
  • a goal of the gaze tracker is to determine where the user is looking, within the boundary of a computer display, by the appearance of eye images detected by a monitoring camera.
  • Figure 1 A shows an example of the physical configuration of the gaze tracker.
  • a video camera 100 of the kind used for video conferencing is mounted on the display screen 101 of a computer workstation W in order to detect an eye of a user 102.
  • the workstation includes a conventional processor 103 and keyboard 104.
  • the task performed by the gaze tracker can be considered as a simulated forward-pass mapping process from a segmented eye image space, to a predefined coordinate space such as the grid matrix shown in
  • mapping function between an eye appearance and its corresponding gaze point is still highly nonlinear and very complicated. This complexity arises from uncertainties and noise encountered at every processing/modelling stage. In particular, for instance, it can arise from errors in eye segmentation, the user's head movement, changes of the eye image depth relative to the camera, decorations around the eye, such as glasses or pencilled eyebrows, and changes in ambient lighting conditions.
  • embodiments of the present invention work in an office environment with the simple video camera 100 mounted on the right side of the display screen 101 of the workstation W, to monitor the user's face continuously. There is no specialised hardware, such as a lighting source, involved. The user sits comfortably at a distance of about 22 to 25 inches away from the screen. He is allowed to move his head freely while looking at the screen, but needs to keep it within the field of view of the camera, and to keep his face within a search window overlaid on the image. 8
  • the neural network based gaze tracker takes the output of an ordinary video camera 1 00, as might be used for video conferencing, and feeds it to the following functional processing blocks:
  • the switch 1 20 takes the output of the histogram normalisation unit 1 1 5 and feeds it to a learning node 130 when the modeller 1 25 is in training mode, or to a real time running node 1 35 when the modeller 125 has been trained and is to be used for detecting gaze co-ordinates.
  • the analogue video signal from a low cost video camera 100 is captured and digitised by the image acquisition and display unit 105 using the SunVideo Card, a video capture and compression card for Sun SPARCstations, and the XIL imaging foundation library developed by SunSoft. (The library is described in Pratt, W.K. (1 997): "Developing Visual Applications: XIL— An Imaging Foundation Library” - published by Sun Microsystems Press).
  • XIL is a cross-platform C functions library that supports a range of video and imaging requirements. For the purpose of simplicity, only grayscale images are used in embodiments of the present invention described herein.
  • Colour images may however be used in enhancements of the system as colours contain some unique features that are otherwise not available from grayscale images, as is shown in the recent work of Oliver and Pentland (1 997), published in "LAFTER: Lips and face real time tracker" - Proc. of Computer Vision and Pattern Recognition Conference, CVPR'97. June 1997, Puerto Rico.
  • the device image of the SunVideo Card which is a 3-banded 8-bit image in YUV colorspace, sized 768 x 576 pixels for PAL, is converted into an 8-bit grayscale image and scaled to the size of 1 92 pixels in width and 144 pixels in 9
  • the maximum capture rate of the SunVideo Card is 25 fps for PAL.
  • the objective of this processing is first to detect the small darkest region in the pupil of the eye, and then go on to segment the proper eye image.
  • the fixed search window 205 shown in Figure 2 is started in the centre part of the grabbed image 200. Inside this search window 205, the image 200 is iteratively thresholded, initially with a lower threshold T 0 .
  • T 0 a threshold for a gaze tracking task of different purpose, published by Stiefelhagen, R., J. Yang, and A. Waibel (1996): “Gaze tracking for multimodal human-computer interaction", Proc. of IEEE Joint Symposia on Intelligence and Systems.
  • Morphological filters are used to remove noise or fill "gaps" of the generated binary image which is then searched pixel by pixel from top left to bottom right.
  • Individual objects comprising pixel clusters, are found and labelled using the 4-connectivity algorithm described in Jain, R., R. Kasturi, and B.G. Schunck (1995): “Machine Vision", published by McGraw-Hill and MIT Press.
  • a rectangular blob is used to represent each found object. Unless a reasonable number of objects of appropriate size are found, the threshold T 0 is increased by a margin to T , and the search process above is repeated.
  • the number of blobs thus obtained are first merged when appropriate, based on adjacency requirements. Heuristics are then used to filter the remaining blobs and identify the one most likely to be part of the pupil of the eye.
  • the heuristics which have been found useful include: 1 ) the number of detected pixels in each blob, roughly in the range (15, 100)
  • the found pupil is then expanded proportionally, based on local information, to the size of 40 by 15 pixels to contain the cornea and the whole eye socket.
  • Figure 3 shows an example of the segmented right eye image 300. (The right eye only is used in the embodiment of the present invention described herein but either eye could of course be used.)
  • the eye image segmentation approach described above is not very sensitive to changes in lighting conditions as long as the face is well lit (sometimes assisted by an ordinary desk lamp). It is not generally affected by the glasses the user is wearing either although, occasionally, strong reflections off the glasses and the appearance of the frame of the glasses in the segmented eye images due to the head moving away from the camera are problematic. They contribute a burst of noise which disrupts features in activation patterns (discussed below) to be sent to the purpose built neural network modelling system 1 25.
  • Histogram normalisation The segmented grayscale eye image, having a value between 0 and 255 for each pixel, is preprocessed by algorithms.
  • the preprocessing algorithms should be simple, reliable and computationally not intensive. For instance, the algorithms might assume a value between -1 .0 and 1 .0 for each pixel.
  • a neural network can then effectively discover the features inherent in the data and learn to associate these features and their distributions with the correct gaze points on the screen. Through adequate training, the network can then be endowed with the power to generalise to data that was not previously present. That is, it can use data learned in respect of similar scenarios and generate its own gaze point data from input data not previously encountered.
  • the histogram normalisation block 1 1 5 takes as input the individual 40 times
  • the vertical axis gives the number of pixels and the horizontal axis shows the grey levels over the range between 0 and 255, partitioned into 64 bins.
  • and t u respectively, have grey scale values at 36 and 144.
  • the region between the bounds is linearised (see below).
  • the activation patterns thus generated, with associated properly coded output gaze points (discussed below), are ready for use in training a neural network. In real time operation mode, these patterns are inputs to the system for gaze prediction.
  • Figure 6 shows the same eye image as in Figure 3 after histogram normalisation. It illustrates that the contrast between important features (the eye socket, pupil, the reflection spot) has been significantly enhanced.
  • the central part of the gaze tracking system is the neural network based modeller/tracker 1 25.
  • the neural network is implemented in software and runs on workstation W although hardware net implementations can be used e.g. optical neural nets, as known in the art.
  • 1 2
  • a suitable neural network is shown in Figure 7. This is a three-layer feedforward neural network with 600 input retina units 700, each receiving a normalised activation value from the segmented 40 x 1 5 eye image.
  • Figure 8 shows a matrix of 50 x 40 grids laid over a display screen 800 displayed on the display 1 01 of Figure 1 A, for guiding the movements of a moving cursor and indicating the gaze position in order to collect the training data of eye image/gaze co-ordinate pairs for the neural network described.
  • this can correspond to dividing the display screen 800 uniformly into a rectilinear matrix of 50 by 40 grids, each sized about 23 by 22 pixels on the display.
  • the resolution of the grid matrix 50 times 40
  • the viewing objects in an application are to appear in only part of the display screen 800, it suffices to collect the data (discussed below in the "Training Data Collection” Section) from this part of the screen and use them for training the model.
  • the co-ordinates of an arbitrary gaze point 805 in this grid matrix can be a value between 0 and 49 along the "x" direction and between 0 and 39 along the "y” direction, with the origin being in the top left corner (0,0) of the screen.
  • mapping function simulation task of embodiments of the present invention demand a gradual change in output representations when the data examples (eye appearance) in input data space exhibits slight difference. This preservation of topological relationships after data transformation (mapping) is the main concern in selecting an output coding mechanism.
  • the Gaussian function used is of
  • the Gaussian shaped activation pattern G(n-n 0 ) is moved across the output units for the x- coordinate by changing n 0 from 0 to 49.
  • a least-square fitting procedure is performed at each unit position to try to match the actual output activation pattern.
  • the peak of the Gaussian shaped pattern that achieves the smallest error determines the horizontal position of the gaze point.
  • the vertical position of the gaze point across the 40 output units for the y-co-ordinates can be found.
  • This section describes a means of collecting correct training data, the process of training a large neural network, analysing the significance of the learned connection weights and briefing the features regarding the real-time gaze tracking system.
  • the user is asked to visually track a blob cursor which travels along the grid matrix on the computer screen in one of the two predefined paths, obtaining horizontal/vertical zig-zag movements.
  • the travelling speed of the cursor can be adjusted to accommodate the acuity of the user's eye reaction time so that s(he) can faithfully and comfortably follow the moving cursor.
  • the size of the blob or the resolution of the grid matrix (for indicating the position of the cursor) on the screen depends on the requirements of an envisaged application and the trade-off between running speed, system complexity and prediction accuracy. In the training phase, the smaller the blob is, the more images need to be collected in one session for the cursor to sweep through the entire screen grid matrix. 1 5
  • the neural network (described above) would have to make provisions for more output units to encode all the possible cursor positions.
  • the user can playback the downloaded image sequence at a selected speed and, if desired, visually examine and identify those noisy examples.
  • the AGD measures the average difference between the current gaze predictions and the desired gaze positions for the training set, excluding a few wild cards due to the user's unexpected eye movements.
  • this first strategy consists of a fast search phase followed by a fine tuning phase.
  • the network is updated in terms of its weighting functions once for every few tens of training examples (typically between 1 0 and 30) which are drawn at random from the entire training data set. (It was found repeatedly that a training process taking examples in their original collection order would always fail to reach a satisfactory convergence state of the neural network, due to perhaps the network's catastrophic 'forgetting' factor.)
  • a small offset ⁇ 0.05 is added to the derivative of each unit's transfer function to speed up the learning process. This is especially useful when a unit's output approaches the saturation limits, either -1 or 1 , of the hyperbolic tangent function. Besides, for each input training pattern random Gaussian noise is added, corresponding to 5% of the size of each retina input. This is particularly effective for overcoming the over-fitting problem in training a neural network and achieving better generalisation performance. In so doing, the neural network, albeit over ten thousand weights, would always approach a quite satisfactory solution after between 50 and 80 training epochs.
  • the network weights are updated once after presenting the whole training set.
  • the nominal learning rate to use is proportionally much smaller than in the first phase, and a slightly smaller magnitude of Gaussian noise, around 3% of each retina input, is used. After about 30 epochs, the system can settle down to a very robust solution. 17
  • An independent validation set is set apart by randomly choosing original examples collected.
  • the validation data set is involved to monitor the progress of the learning process in order to prevent the network from overfitting the training data.
  • the learning process stops when the validation error starts to pick up or saturate after a previous general downwards trend.
  • the weight set obtained at this point will be used to drive the real-time gaze tracking system. In practice, however, several trials with different initial weights are needed to find out the weight set with the smallest validation error, which is expected to provide better generalisation performance.
  • Figure 1 1 plots the curves for learning and its corresponding validation errors versus the number of training epochs in one trial of simulations of the neural network. (The ripples on the curves are due to the training scheme of using randomly chosen examples within each epoch and the way of updating the weights once for every ten examples instead of the whole training set of 2,800 examples.) Following the Section on data collection above, 1606 and 1 635 valid paired examples have been collected, respectively, from observing the horizontal and vertical zig-zag cursor movement path, from which 206 and 235 randomly

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Abstract

La présente invention concerne un dispositif de suivi du regard destiné à une interface utilisateur multimodale. Ce dispositif utilise en poste standard de vidéoconférence équipant une station de travail pour déterminer la direction dans laquelle porte le regard de l'utilisateur sur un écran. Le dispositif de suivi du regard utilise une caméra vidéo (100) donnant une image quantifiée de l'oeil de l'utilisateur. Une fois que la pupille a été détectée, on utilise un réseau neural (125) pour apprendre au dispositif de suivi du regard à détecter la direction du regard. On peut utiliser un préprocesseur (115) pour améliorer l'entrée faite au réseau neural. Un réseau de Bayes (140) permet de faire l'apprentissage des relations entre le temps de réponse et la précision de façon à obtenir une sortie du réseau neural permettant la prise en compte des préférences extérieurement définies d'un utilisateur.
PCT/GB1998/003441 1997-11-17 1998-11-16 Interface utilisateur WO1999026126A1 (fr)

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Application Number Priority Date Filing Date Title
AU11657/99A AU1165799A (en) 1997-11-17 1998-11-16 User interface
EP98954602A EP1032872A1 (fr) 1997-11-17 1998-11-16 Interface utilisateur

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GB9724277.0 1997-11-17
GBGB9724277.0A GB9724277D0 (en) 1997-11-17 1997-11-17 User interface
EP98306261 1998-08-05
EP98306261.3 1998-08-05

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EP1324259A1 (fr) * 2000-08-09 2003-07-02 Matsushita Electric Industrial Co., Ltd. Procédé et dispositif pour détecter la position des yeux
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WO2007015200A2 (fr) * 2005-08-04 2007-02-08 Koninklijke Philips Electronics N.V. Appareil permettant de surveiller une personne ayant un interet pour un objet et procede associe
WO2009154484A2 (fr) * 2008-06-20 2009-12-23 Business Intelligence Solutions Safe B.V. Procédés, dispositifs et systèmes de visualisation de données et applications connexes
EP1589485A3 (fr) * 2004-04-20 2012-09-12 Delphi Technologies, Inc. Procédé de poursuite d'objet et d'identification de l'état d'un oeuil
US20170308734A1 (en) * 2016-04-22 2017-10-26 Intel Corporation Eye contact correction in real time using neural network based machine learning
WO2017208227A1 (fr) 2016-05-29 2017-12-07 Nova-Sight Ltd. Système et procédé d'affichage
WO2018005594A1 (fr) * 2016-06-28 2018-01-04 Google Llc Suivi du regard à l'aide de réseaux neuronaux
IT201800002114A1 (it) * 2018-01-29 2019-07-29 Univ Degli Studi Roma La Sapienza Procedimento indirizzato a pazienti con disabilita' motorie per scegliere un comando mediante un'interfaccia grafica, relativo sistema e prodotto informatico
WO2019154511A1 (fr) * 2018-02-09 2019-08-15 Pupil Labs Gmbh Dispositifs, systèmes et procédés de prédiction de paramètres liés au regard à l'aide d'un réseau neuronal
CN110516282A (zh) * 2019-07-03 2019-11-29 杭州电子科技大学 一种基于贝叶斯统计的磷化铟晶体管建模方法
US10664949B2 (en) 2016-04-22 2020-05-26 Intel Corporation Eye contact correction in real time using machine learning
US10846877B2 (en) 2016-06-28 2020-11-24 Google Llc Eye gaze tracking using neural networks
US10866635B2 (en) 2018-09-13 2020-12-15 Toyota Research Institute, Inc. Systems and methods for capturing training data for a gaze estimation model
CN112559099A (zh) * 2020-12-04 2021-03-26 北京新能源汽车技术创新中心有限公司 基于用户行为的远程图像显示方法、装置、系统及存储介质
WO2021127005A1 (fr) * 2019-12-16 2021-06-24 Nvidia Corporation Détermination du regard utilisant l'éblouissement en tant qu'entrée
US11132543B2 (en) 2016-12-28 2021-09-28 Nvidia Corporation Unconstrained appearance-based gaze estimation
US11194161B2 (en) 2018-02-09 2021-12-07 Pupil Labs Gmbh Devices, systems and methods for predicting gaze-related parameters
US11393251B2 (en) 2018-02-09 2022-07-19 Pupil Labs Gmbh Devices, systems and methods for predicting gaze-related parameters
US11537202B2 (en) 2019-01-16 2022-12-27 Pupil Labs Gmbh Methods for generating calibration data for head-wearable devices and eye tracking system
US11676422B2 (en) 2019-06-05 2023-06-13 Pupil Labs Gmbh Devices, systems and methods for predicting gaze-related parameters
US12140771B2 (en) 2020-02-19 2024-11-12 Pupil Labs Gmbh Eye tracking module and head-wearable device

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