Monocular gaze depth estimation using the vestibulo-ocular reflex

Gaze depth estimation presents a challenge for eye tracking in 3D. This work investigates a novel approach to the problem based on eye movement mediated by the vestibulo-ocular reflex (VOR). VOR stabilises gaze on a target during head movement, with eye movement in the opposite direction, and the VOR gain increases the closer the fixated target is to the viewer. We present a theoretical analysis of the relationship between VOR gain and depth which we investigate with empirical data collected in a user study (N=10). We show that VOR gain can be captured using pupil centres, and propose and evaluate a practical method for gaze depth estimation based on a generic function of VOR gain and two-point depth calibration. The results show that VOR gain is comparable with vergence in capturing depth while only requiring one eye, and provide insight into open challenges in harnessing VOR gain as a robust measure

Towards binocular active vision in a robot head system

This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the ‘searchlight metaphor’ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a “stepping stone” visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade

Using natural versus artificial stimuli to perform calibration for 3D gaze tracking

International audienceThe presented study tests which type of stereoscopic image, natural or artificial, is more adapted to perform efficient and reliable calibration in order to track the gaze of observers in 3D space using classical 2D eye tracker. We measured the horizontal disparities, i.e. the difference between the x coordinates of the two eyes obtained using a 2D eye tracker. This disparity was recorded for each observer and for several target positions he had to fixate. Target positions were equally distributed in the 3D space, some on the screen (with a null disparity), some behind the screen (uncrossed disparity) and others in front of the screen (crossed disparity). We tested different regression models (linear and non linear) to explain either the true disparity or the depth with the measured disparity. Models were tested and compared on their prediction error for new targets at new positions. First of all, we found that we obtained more reliable disparities measures when using natural stereoscopic images rather than artificial. Second, we found that overall a non-linear model was more efficient. Finally, we discuss the fact that our results were observer dependent, with variability's between the observer's behavior when looking at 3D stimuli. Because of this variability, we proposed to compute observer specific model to accurately predict their gaze position when exploring 3D stimuli

Gaze Estimation Technique for Directing Assistive Robotics

AbstractAssistive robotics may extend capabilities for individuals with reduced mobility or dexterity. However, effective use of robotic agents typically requires the user to issue control commands in the form of speech, gesture, or text. Thus, for unskilled or impaired users, the need for a paradigm of intuitive Human-Robot Interaction (HRI) is prevalent. It can be inferred that the most productive interactions are those in which the assistive agent is able to ascertain the intention of the user. Also, to perform a task, the agent must know the user's area of attention in three-dimensional space. Eye gaze tracking can be used as a method to determine a specific Volume of Interest (VOI). However, gaze tracking has heretofore been under-utilized as a means of interaction and control in 3D space. This research aims to determine a practical volume of interest in which an individual's eyes are focused by combining past methods in order to achieve greater effectiveness. The proposed method makes use of eye vergence as a useful depth discriminant to generate a tool for improved robot path planning. This research investigates the accuracy of the Vector Intersection (VI) model when applied to a usably large workspace volume. A neural network is also used in tandem with the VI model to create a combined model. The output of the combined model is a VOI that can be used as an aid in a number of applications including robot path planning, entertainment, ubiquitous computing, and others

A technique for estimating three-dimensional volume-of-interest using eye gaze

Assistive robotics promises to be of use to those who have limited mobility or dexterity. Moreover, those who have limited movement of limbs can benefit greatly from such assistive devices. However, to use such devices, one would need to give commands to an assistive agent, often in the form of speech, gesture, or text. The need for a more convenient method of Human-Robot Interaction (HRI) is prevalent, especially for impaired users because of severe mobility constraints. For a socially responsive assistive device to be an effective aid, the device generally should understand the intention of the user. Also, to perform a task based on gesture, the assistive device requires the user's area of attention in three-dimensional (3D) space. Gaze tracking can be used as a method to determine a specific volume of interest (VOI). However, heretofore gaze tracking has been under-utilized as a means of interaction and control in 3Dspace. The main objective of this research is to determine a practical VOI in which an individual's eyes are focused by combining existing methods. Achieving this objective sets a foundation for further use of vergence data as a useful discriminant to generate a proper directive technique for assistive robotics. This research investigates the accuracy of the Vector Intersection (VI) model when applied to a usable workspace. A neural network is also applied to gaze data for use in tandem with the VI model to create a Combined Model. The output of the Combined Model is a VOI that can be used to aid in a number of applications including robot path planning, entertainment, ubiquitous computing, and others. An alternative Search Region method is investigated as well

The mean point of vergence is biased under projection

The point of interest in three-dimensional space in eye tracking is often computed based on intersecting the lines of sight with geometry, or finding the point closest to the two lines of sight. We first start by theoretical analysis with synthetic simulations. We show that the mean point of vergence is generally biased for centrally symmetric errors and that the bias depends on the horizontal vs. vertical error distribution of the tracked eye positions. Our analysis continues with an evaluation on real experimental data. The error distributions seem to be different among individuals but they generally leads to the same bias towards the observer. And it tends to be larger with an increased viewing distance. We also provided a recipe to minimize the bias, which applies to general computations of eye ray intersection. These findings not only have implications for choosing the calibration method in eye tracking experiments and interpreting the observed eye movements data; but also suggest to us that we shall consider the mathematical models of calibration as part of the experiment

Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research

The Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings