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.
5 PAPERS • NO BENCHMARKS YET
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.
2 PAPERS • NO BENCHMARKS YET
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.
1 PAPER • NO BENCHMARKS YET