Papers with Code 아카이브

Gaze Target Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
2
보존된 평가 기록
2

Gaze Target Estimation refers to predicting the image 2D gaze location of a person in the image.

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

GazeFollow

GazeFollow is a large-scale dataset annotated with the location of where people in images are looking. It uses several major datasets that contain people as a source of images: 1, 548 images from SUN, 33, 790 images from MS COCO, 9, 135 images from Actions 40, 7, 791 images from PASCAL, 508 images from the ImageNet detection challenge and 198, 097 images from the Places dataset. This concatenation results in a challenging and large image collection of people performing diverse activities in many everyday scenarios. Source: GazeFollow

VideoAttentionTarget

A dataset with fully annotated attention targets in video for attention target estimation.

EyeInfo

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.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.