Subject: Computer and Information Science
Publication Year: 2022
Funding Information Agency: JST CREST research grant, Japan
1 to 5 of 5 Results
Nov 30, 2022 - Perceptual User Interfaces
Bulling, Andreas, 2022, "DEyeAdicContact", https://doi.org/10.18419/darus-3289, DaRUS, V1, UNF:6:7QxaU+oeOPfaI8gMpCn5cw== [fileUNF]
We created our own dataset of natural dyadic interactions with fine-grained eye contact annotations using videos of dyadic interviews published on YouTube. Especially compared to lab-based recordings, these Youtube interviews allow us to analyse behaviour in a natural situation.... |
Nov 30, 2022 - Perceptual User Interfaces
Bulling, Andreas, 2022, "MPIIMobileAttention", https://doi.org/10.18419/darus-3285, DaRUS, V1
This is a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). |
Nov 30, 2022 - Perceptual User Interfaces
Bulling, Andreas, 2022, "MPIIPrivacEye", https://doi.org/10.18419/darus-3286, DaRUS, V1
First-person video dataset recorded in daily life situations of 17 participants, annotated by themselves for privacy sensitivity. The dataset of Steil et al. contains more than 90 hours of data recorded continuously from 20 participants (six females, aged 22-31) over more than fo... |
Oct 31, 2022 - Perceptual User Interfaces
Bulling, Andreas, 2022, "MPIIEgoFixation", https://doi.org/10.18419/darus-3234, DaRUS, V1, UNF:6:fcDo56Ha9jxYApA9klubEQ== [fileUNF]
This dataset is made up of handmade fixation annotations for a subset of the private dataset created in Yusuke Sugano and Andreas Bulling. 2015. Self-calibrating head-mounted eye trackers using egocentric visual saliency. In Proceedings of the 28th Annual ACM Symposium on User In... |
Oct 28, 2022 - Perceptual User Interfaces
Bulling, Andreas, 2022, "MPIIDPEye: Privacy-Aware Eye Tracking Using Differential Privacy", https://doi.org/10.18419/darus-3235, DaRUS, V1, UNF:6:1+E3rPNFm33/BHVCDizlJA== [fileUNF]
We designed a privacy-aware VR interface that uses differential privacy, which we evaluate on a new 20-participant dataset for two privacy sensitive tasks. The data consists of eye gaze as participants read different types of documents. The dataset consists of a .zip file with tw... |