Gaze Prediction

OpenEDS2020 is a dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. Source: OpenEDS2020: Open Eyes Dataset Image Source: Palmero et al

GOO (Gaze-on-Objects) is a dataset for gaze object prediction, where the goal is to predict a bounding box for a person's gazed-at object. GOO is composed of a large set of synthetic images (GOO Synth) supplemented by a smaller subset of real images (GOO-Real) of people looking at objects in a retail environment.

Is one of the largest egocentric datasets in the object search task with eyetracking information available Source: Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks

To study the problem of weakly supervised attended object detection in cultural sites, we collected and labeled a dataset of egocentric images acquired from subjects visiting a cultural site. The dataset has been designed to offer a snapshot of the subject’s visual experience while visiting a museum and contains labels for several artworks and details attended by the subjects.

EgoMon Gaze & Video Dataset is an Egocentric (first person) Dataset that consists of 7 videos of 30 minutes, more or less, each one of them. - 7 videos with the gaze information plotted on them. - The same videos (without the gaze information plotted on them). - A total of 13428 images, more or less, that corresponds to each frame per second of all these videos. - 7 text files with the gaze data extracted from each video.

The EyeInfo Dataset is an open-source eye-tracking dataset created by Fabricio Batista Narcizo, a research scientist at the IT University of Copenhagen (ITU) and GN Audio A/S (Jabra), Denmark. This dataset was introduced in the paper "High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods" (DOI: 10.3390/vision5030041). The dataset contains high-speed monocular eye-tracking data from an off-the-shelf remote eye tracker using active illumination. The data from each user has a text file with data annotations of eye features, environment, viewed targets, and facial features. This dataset follows the principles of the General Data Protection Regulation (GDPR). We have built a remote eye tracker with off-the-shelf components to collect the real eye-tracking data. The collected data contain binocular eye information from 83 participants (166 trials), with the following annotations: frame number, target ID, timestamp, viewed target coordinates, pupil center, the major/minor axes and angle orientation of fitted ellipse, and four enumerated corneal reflections’ coordinates. We have extracted the eye features from recorded eye videos using a feature-based eye-tracking method (i.e., binarization+fitting ellipse), and the raw data are available on individual annotated text files (CSV). The raw dataset contains outliers due to blinks, light reflections, missing glints, and low contrast between the iris and pupil.