Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction

By Moritz Kassner, William Patera, Andreas Bulling. UbiComp Adjunct, 2014

897 citations recorded by the source corpus.

In this paper we present Pupil -- an accessible, affordable, and extensible open source platform for pervasive eye tracking and gaze-based interaction. Pupil comprises 1) a light-weight eye tracking headset, 2) an open source software framework for mobile eye tracking, as well as 3) a graphical user interface to playback and visualize video and gaze data. Pupil features high-resolution scene and eye cameras for monocular and binocular gaze estimation. The software and GUI are platform-independent and include state-of-the-art algorithms for real-time pupil detection and tracking, calibration, and accurate gaze estimation. Results of a performance evaluation show that Pupil can provide an average gaze estimation accuracy of 0.6 degree of visual angle (0.08 degree precision) with a processing pipeline latency of only 0.045 seconds.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/98c92ffa5bd1de5c1db3dfd7881c494b5fd3e7b2

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In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

In Human-Computer Interaction

Citations in the corpus

HARMONIC: A multimodal dataset of assistive human–robot collaboration (2018, methodology): “Eye gaze Participant eye gaze direction was captured by a Pupil Labs Pupil (Pupil Labs, Inc. 2017; Kassner et al. 2014) sensor.”
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments (2018, methodology): “A RGB-D camera records a set of images of a subject wearing Pupil Labs mobile eyetracking glasses [24].”
Towards Explainable Shared Control using Augmented Reality (2019, methodology): “5◦ FoV), which has been supplemented with an add-on for eye tracking [28].”
Pinpointing: Precise Head- and Eye-Based Target Selection for Augmented Reality (2018, background): “With wearable eye-tracking devices becoming affordable to use in combination with head-worn displays (e.g. Pupil Labs [32,51], FOVE [19]), researchers are increasingly exploring wearable eye gaze input [50,55].”
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (2017, methodology): “We implemented custom software running as a background service on participants’ laptops, and opted to use the well-established moving dot stimulus [59], to collect”