Low-Cost Based Eye Tracking and Eye Gaze Estimation

The costs of current gaze tracking systems remain too high for general public use. The main reason for this is the cost of parts, especially high-quality cameras and lenses and cost development. This research build the low cost based for gaze tracking system. The device is built by utilizing of modified web camera in infrared spectrum. A new technique is also proposed here in order to detect the center pupil coordinate based on connected component labeling. By combination the pupils coordinate detection method with third order polynomial regression in calibration process to determine the gaze point. The experiment results show our system has an acceptable accuracy rate with error pixel 0.39o in visual degree

An empirical investigation of gaze selection in mid-air gestural 3D manipulation

In this work, we investigate gaze selection in the context of mid-air hand gestural manipulation of 3D rigid bodies on monoscopic displays. We present the results of a user study with 12 participants in which we compared the performance of Gaze, a Raycasting technique (2D Cursor) and a Virtual Hand technique (3D Cursor) to select objects in two 3D mid-air interaction tasks. Also, we compared selection confirmation times for Gaze selection when selection is followed by manipulation to when it is not. Our results show that gaze selection is faster and more preferred than 2D and 3D mid-air-controlled cursors, and is particularly well suited for tasks in which users constantly switch between several objects during the manipulation. Further, selection confirmation times are longer when selection is followed by manipulation than when it is not

Asservissement d'un bras robotique d'assistance à l'aide d'un système de stéréo vision artificielle et d'un suiveur de regard

RÉSUMÉ L’utilisation récente de bras robotiques sériels dans le but d’assister des personnes ayant des problèmes de motricités sévères des membres supérieurs soulève une nouvelle problématique au niveau de l’interaction humain-machine (IHM). En effet, jusqu’à maintenant le « joystick » est utilisé pour contrôler un bras robotiques d’assistance (BRA). Pour les utilisateurs ayant des problèmes de motricité sévères des membres supérieurs, ce type de contrôle n’est pas une option adéquate. Ce mémoire présente une autre option afin de pallier cette problématique. La solution présentée est composée de deux composantes principales. La première est une caméra de stéréo vision utilisée afin d’informer le BRA des objets présents dans son espace de travail. Il est important qu’un BRA soit conscient de ce qui est présent dans son espace de travail puisqu’il doit être en mesure d’éviter les objets non voulus lorsqu’il parcourt un trajet afin d’atteindre l’objet d’intérêt pour l'utilisateur. La deuxième composante est l’IHM qui est dans ce travail représentée par un suiveur de regard à bas coût. Effectivement, le suiveur de regard a été choisi puisque, généralement, les yeux d’un patient ayant des problèmes sévères de motricités au niveau des membres supérieurs restent toujours fonctionnels. Le suiveur de regard est généralement utilisé avec un écran pour des applications en 2D ce qui n’est pas intuitif pour l’utilisateur puisque celui-ci doit constamment regarder une reproduction 2D de la scène sur un écran. En d’autres mots, il faut rendre le suiveur de regard viable dans un environnement 3D sans l’utilisation d’un écran, ce qui a été fait dans ce mémoire. Un système de stéréo vision, un suiveur de regard ainsi qu’un BRA sont les composantes principales du système présenté qui se nomme PoGARA qui est une abréviation pour Point of Gaze Assistive Robotic Arm. En utilisant PoGARA, l’utilisateur a été capable d’atteindre et de prendre un objet pour 80% des essais avec un temps moyen de 13.7 secondes sans obstacles, 15.3 secondes avec un obstacle et 16.3 secondes avec deux obstacles.----------ABSTRACT The recent increased interest in the use of serial robots to assist individuals with severe upper limb disability brought-up an important issue which is the design of the right human computer interaction (HCI). Indeed, so far, the control of assistive robotic arms (ARA) is often done using a joystick. For the users who have a severe upper limb disability, this type of control is not a suitable option. In this master’s thesis, a novel solution is presented to overcome this issue. The developed solution is composed of two main components. The first one is a stereo vision system which is used to inform the ARA of the content of its workspace. It is important for the ARA to be aware of what is present in its workspace since it needs to avoid the unwanted objects while it is on its way to grasp the object of interest. The second component is the actual HCI, where an eye tracker is used. Indeed, the eye tracker was chosen since the eyes, often, remain functional even for patients with severe upper limb disability. However, usually, low-cost, commercially available eye trackers are mainly designed for 2D applications with a screen which is not intuitive for the user since he needs to constantly watch a reproduction of the scene on a 2D screen instead of the 3D scene itself. In other words, the eye tracker needs to be made viable for usage in a 3D environment without the use of a screen. This was achieved in this master thesis work. A stereo vision system, an eye tracker as well as an ARA are the main components of the developed system named PoGARA which is short for Point of Gaze Assistive Robotic Arm. Using PoGARA, during the tests, the user was able to reach and grasp an object for 80% of the trials with an average time of 13.7 seconds without obstacles, 15.3 seconds with one obstacles and 16.3 seconds with two obstacles

Sampling rate influences saccade detection in mobile eye tracking of a reading task

The purpose of this study was to compare saccade detection characteristics in two mobile eye trackers with different sampling rates in a natural task. Gaze data of 11 participants were recorded in one 60 Hz and one 120 Hz mobile eye tracker and compared directly to the saccades detected by a 1000 HZ stationary tracker while a reading task was performed. Saccades and fixations were detected using a velocity based algorithm and their properties analyzed. Results showed that there was no significant difference in the number of detected fixations but mean fixation durations differed between the 60 Hz mobile and the stationary eye tracker. The 120 Hz mobile eye tracker showed a significant increase in the detection rate of saccades and an improved estimation of the mean saccade duration, compared to the 60 Hz eye tracker. To conclude, for the detection and analysis of fast eye movements, such as saccades, it is better to use a 120 Hz mobile eye tracker

Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality

[EN] Fixation identification is an essential task in the extraction of relevant information from gaze patterns; various algorithms are used in the identification process. However, the thresholds used in the algorithms greatly affect their sensitivity. Moreover, the application of these algorithm to eye-tracking technologies integrated into head-mounted displays, where the subject's head position is unrestricted, is still an open issue. Therefore, the adaptation of eye-tracking algorithms and their thresholds to immersive virtual reality frameworks needs to be validated. This study presents the development of a dispersion-threshold identification algorithm applied to data obtained from an eye-tracking system integrated into a head-mounted display. Rules-based criteria are proposed to calibrate the thresholds of the algorithm through different features, such as number of fixations and the percentage of points which belong to a fixation. The results show that distance-dispersion thresholds between 1-1.6 degrees and time windows between0.25-0.4s are the acceptable range parameters, with 1 degrees and0.25s being the optimum. The work presents a calibrated algorithm to be applied in future experiments with eye-tracking integrated into head-mounted displays and guidelines for calibrating fixation identification algorithmsWe thank Pepe Roda Belles for the development of the virtual reality environment and the integration of the HMD with Unity platform. We also thank Masoud Moghaddasi for useful discussions and recommendations.Llanes-Jurado, J.; Marín-Morales, J.; Guixeres Provinciale, J.; Alcañiz Raya, ML. (2020). Development and Calibration of an Eye-Tracking Fixation Identification Algorithm for Immersive Virtual Reality. Sensors. 20(17):1-15. https://doi.org/10.3390/s20174956S1152017Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). 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Voila-A: Aligning Vision-Language Models with User's Gaze Attention

In recent years, the integration of vision and language understanding has led to significant advancements in artificial intelligence, particularly through Vision-Language Models (VLMs). However, existing VLMs face challenges in handling real-world applications with complex scenes and multiple objects, as well as aligning their focus with the diverse attention patterns of human users. In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications. First, we collect hundreds of minutes of gaze data to demonstrate that we can mimic human gaze modalities using localized narratives. We then design an automatic data annotation pipeline utilizing GPT-4 to generate the VOILA-COCO dataset. Additionally, we innovate the Voila Perceiver modules to integrate gaze information into VLMs while preserving their pretrained knowledge. We evaluate Voila-A using a hold-out validation set and a newly collected VOILA-GAZE Testset, which features real-life scenarios captured with a gaze-tracking device. Our experimental results demonstrate that Voila-A significantly outperforms several baseline models. By aligning model attention with human gaze patterns, Voila-A paves the way for more intuitive, user-centric VLMs and fosters engaging human-AI interaction across a wide range of applications

Gaze-based Interaction for Virtual Environments

Abstract We present an alternative interface that allows users to perceive new sensations in virtual environments. Gaze-based interaction in virtual environments creates the feeling of controlling objects with the mind, arguably translating into a more intense immersion sensation. Additionally, it is also free of some of the most cumbersome aspects of interacting in virtual worlds. By incorporating a real-time physics engine, the sensation of moving something real is further accentuated. We also describe various simple yet effective techniques that allow eyetracking devices to enhance the three-dimensional visualization capabilities of current displays. Some of these techniques have the additional advantage of freeing the mouse from most navigation tasks. This work focuses on the study of existing techniques, a detailed description of the implemented interface and the evaluation (both objective and subjective) of the interface. Given that appropriate filtering of the data from the eye tracker used is a key aspect for the correct functioning of the interface, we will also discuss that aspect in depth

Eye tracking in virtual reality: Vive pro eye spatial accuracy, precision, and calibration reliability

A growing number of virtual reality devices now include eye tracking technology, which can facilitate oculomotor and cognitive research in VR and enable use cases like foveated rendering. These applications require different tracking performance, often measured as spatial accuracy and precision. While manufacturers report data quality estimates for their devices, these typically represent ideal performance and may not reflect real-world data quality. Additionally, it is unclear how accuracy and precision change across sessions within the same participant or between devices, and how performance is influenced by vision correction. Here, we measured spatial accuracy and precision of the Vive Pro Eye built-in eye tracker across a range of 30 visual degrees horizontally and vertically. Participants completed ten measurement sessions over multiple days, allowing to evaluate calibration reliability. Accuracy and precision were highest for central gaze and decreased with greater eccentricity in both axes. Calibration was successful in all participants, including those wearing contacts or glasses, but glasses yielded significantly lower performance. We further found differences in accuracy (but not precision) between two Vive Pro Eye headsets, and estimated participants’ inter-pupillary distance. Our metrics suggest high calibration reliability and can serve as a baseline for expected eye tracking performance in VR experiments