Institut für Visualisierung und Interaktive Systeme

The Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart is an institute of the department of computer science in the faculty of computer science, electrical engineering and information technology. Around 70 people are conducting research and teaching in the areas of visualisation and computer graphics, human-computer interaction and cognitive systems, computer vision and pattern recognition as well as augmented and virtual reality. This DataVerse contains the research data produced by the institute in these fields. Large-scale projects – like collaborative research centres – the institute is participating in might have additional DataVerses containing data produced at VIS. Furthermore, we recommend also visiting the DataVerse of VISUS, our closely related central research institute for the area of visualisation.

Computer Vision

Collaborative Artificial Intelligence

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11 to 20 of 33 Results

Ontologies and Rules for Context-Aware Assessment of the Usability of Output Devices and Modalities in Ubiquitous Mobility Systems Jun 16, 2023 Keller, Christine, 2023, "Ontologies and Rules for Context-Aware Assessment of the Usability of Output Devices and Modalities in Ubiquitous Mobility Systems", https://doi.org/10.18419/DARUS-3385, DaRUS, V1 This dataset contains OWL ontologies and SWRL that model the usage context and usability of output devices and modalities for ubiquitous mobility systems. It also contains a usability ontology modeling usability attributes relevant in ubiquitous mobility systems. The SWRL rule set contains assessment rules supporting a usability assessment of outpu...
Source code for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections" May 26, 2023 Hirsch, Alexandra; Franke, Max; Koch, Steffen, 2023, "Source code for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"", https://doi.org/10.18419/DARUS-3540, DaRUS, V1 This repository contains the source code related to an OSF pre-registration for an online study. The goal of the study was to evaluate how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimu...
Stimulus Data for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections" May 26, 2023 Hirsch, Alexandra; Franke, Max; Koch, Steffen, 2023, "Stimulus Data for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"", https://doi.org/10.18419/DARUS-3463, DaRUS, V1 We compare how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimuthal equidistant projection, or two-point equidistant projection. The distances of the start and end point are varied. Map t...
MPIIFaceGaze Mar 14, 2023 - Collaborative Artificial Intelligence Bulling, Andreas, 2023, "MPIIFaceGaze", https://doi.org/10.18419/DARUS-3240, DaRUS, V1 We present the MPIIFaceGaze dataset which is based on the MPIIGaze dataset, with the additional human facial landmark annotation and the face regions available. We added additional facial landmark and pupil center annotations for 37,667 face images. Facial landmarks annotations were conducted in a semi-automatic manner as running facial landmark de...
Labeled pupils in the wild (LPW) Mar 8, 2023 - Collaborative Artificial Intelligence Bulling, Andreas, 2023, "Labeled pupils in the wild (LPW)", https://doi.org/10.18419/DARUS-3237, DaRUS, V1 We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover p...
MPIIEmo Feb 24, 2023 - Collaborative Artificial Intelligence Bulling, Andreas, 2023, "MPIIEmo", https://doi.org/10.18419/DARUS-3287, DaRUS, V1 We present a human-validated dataset that contains 224 high-resolution, multi-view video clips and audio recordings of emotionally charged interactions between eight couples of actors. The dataset is fully annotated with categorical labels for four basic emotions (anger, happiness, sadness, and surprise) and continuous labels for valence, activatio...
DEyeAdicContact Nov 30, 2022 - Collaborative Artificial Intelligence 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. All interviews were conducted via video conferencing and provide front...
MPIIMobileAttention Nov 30, 2022 - Collaborative Artificial Intelligence 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).
MPIIPrivacEye Nov 30, 2022 - Collaborative Artificial Intelligence 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 four hours each. Participants were students with different backgrounds a...
MPIIEgoFixation Oct 31, 2022 - Collaborative Artificial Intelligence 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 Interface Software & Technology. ACM, 363-372. https://doi.org/10.1145/2...

Ontologies and Rules for Context-Aware Assessment of the Usability of Output Devices and Modalities in Ubiquitous Mobility Systems

Jun 16, 2023

Keller, Christine, 2023, "Ontologies and Rules for Context-Aware Assessment of the Usability of Output Devices and Modalities in Ubiquitous Mobility Systems", https://doi.org/10.18419/DARUS-3385, DaRUS, V1

This dataset contains OWL ontologies and SWRL that model the usage context and usability of output devices and modalities for ubiquitous mobility systems. It also contains a usability ontology modeling usability attributes relevant in ubiquitous mobility systems. The SWRL rule set contains assessment rules supporting a usability assessment of outpu...

Source code for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"

May 26, 2023

Hirsch, Alexandra; Franke, Max; Koch, Steffen, 2023, "Source code for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"", https://doi.org/10.18419/DARUS-3540, DaRUS, V1

This repository contains the source code related to an OSF pre-registration for an online study. The goal of the study was to evaluate how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimu...

Stimulus Data for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"

May 26, 2023

Hirsch, Alexandra; Franke, Max; Koch, Steffen, 2023, "Stimulus Data for "Comparative Study on the Perception of Direction in Animated Map Transitions Using Different Map Projections"", https://doi.org/10.18419/DARUS-3463, DaRUS, V1

We compare how well participants can determine the geographical direction of an animated map transition. In our between-subject online study, each of three groups is shown map transitions in one map projection: Mercator, azimuthal equidistant projection, or two-point equidistant projection. The distances of the start and end point are varied. Map t...

MPIIFaceGaze

Mar 14, 2023 - Collaborative Artificial Intelligence

Bulling, Andreas, 2023, "MPIIFaceGaze", https://doi.org/10.18419/DARUS-3240, DaRUS, V1

We present the MPIIFaceGaze dataset which is based on the MPIIGaze dataset, with the additional human facial landmark annotation and the face regions available. We added additional facial landmark and pupil center annotations for 37,667 face images. Facial landmarks annotations were conducted in a semi-automatic manner as running facial landmark de...

Labeled pupils in the wild (LPW)

Mar 8, 2023 - Collaborative Artificial Intelligence

Bulling, Andreas, 2023, "Labeled pupils in the wild (LPW)", https://doi.org/10.18419/DARUS-3237, DaRUS, V1

We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover p...

MPIIEmo

Feb 24, 2023 - Collaborative Artificial Intelligence

Bulling, Andreas, 2023, "MPIIEmo", https://doi.org/10.18419/DARUS-3287, DaRUS, V1

We present a human-validated dataset that contains 224 high-resolution, multi-view video clips and audio recordings of emotionally charged interactions between eight couples of actors. The dataset is fully annotated with categorical labels for four basic emotions (anger, happiness, sadness, and surprise) and continuous labels for valence, activatio...

DEyeAdicContact

Nov 30, 2022 - Collaborative Artificial Intelligence

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. All interviews were conducted via video conferencing and provide front...

MPIIMobileAttention

Nov 30, 2022 - Collaborative Artificial Intelligence

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).

MPIIPrivacEye

Nov 30, 2022 - Collaborative Artificial Intelligence

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 four hours each. Participants were students with different backgrounds a...

MPIIEgoFixation

Oct 31, 2022 - Collaborative Artificial Intelligence

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 Interface Software & Technology. ACM, 363-372. https://doi.org/10.1145/2...

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