Object of Fixation Dataset

datacite.relation.isSupplementTo https://doi.org/10.1109/IVS.2019.8814224
dc.contributor.author Schwehr, Julian
dc.contributor.author Knaust, Moritz
dc.date.accessioned 2020-10-23T12:49:35Z
dc.date.available 2020-10-23T12:49:35Z
dc.date.created 2019-06-10
dc.date.issued 2020-10-23
dc.description This dataset was created in order to evaluate different models for detecting the driver's current object of fixation, i.e. finding the object the driver is looking at, when using a remote gaze tracking system. Determining the tracking quality of the remote gaze tracking system does not assess the advantages and drawbacks of specific algorithmic fusion approaches. Furthermore, when estimating the driver's point of regard (PoR) and the gaze target, all algorithmic approaches share the problem that there exists no ground truth on where the driver is truly looking. For this purpose, a wearable gaze tracking device was operated in parallel to the vehicle-integrated head-eye-tracking system, serving as source for reference data of the driver's visual attention. The dataset contains: * remote gaze direction measurements, stereo image recordings, and object lists of several artificial and real world scenarios as recorded by the PRORETA 4 test vehicle, * images and point of regard as measured by the wearable eye tracking device, * some sequences are labeled as outlined in the associated paper, * raw data of the real world drive (~5min), * more information in the Description.txt of the dataset. If you use this dataset in your research please cite the associated publication: _Julian Schwehr, Moritz Knaust, and Volker Willert: “How to Evaluate Object- of-Fixation Detection”, IEEE Intelligent Vehicles Symposium (IV), 2019._ _[Read the Paper at IEEE Xplore](https://ieeexplore.ieee.org/document/8814224)_ **BibTex:** @inproceedings{Schwehr.2019, author = {Schwehr, Julian and Knaust, Moritz and Willert, Volker}, title = {How to Evaluate Object-of-Fixation Detection}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, year = {2019} } The mentioned gaze target tracking model is introduced in: _[Multi-Hypothesis Multi-Model Driver's Gaze Target Tracking.](https://ieeexplore.ieee.org/document/8569655)_ en_US
dc.description.version Initial version en_US
dc.identifier.uri https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2500
dc.language.iso en en_US
dc.rights.licenseCC-BY-4.0 (https://creativecommons.org/licenses/by/4.0)
dc.subject object-of-fixation detection en_US
dc.subject driver monitoring en_US
dc.subject wearable head eye trackers en_US
dc.subject eye tracking glasses en_US
dc.subject series surround sensors en_US
dc.subject remote eye tracker en_US
dc.subject wearable device en_US
dc.subject advanced driver assistance systems en_US
dc.subject remote gaze tracking systems en_US
dc.subject inside-outside looking systems en_US
dc.subject calibration errors en_US
dc.subject object detection en_US
dc.subject gaze tracking en_US
dc.subject driver information systems en_US
dc.subject.classification 4.41-04
dc.subject.classification 4.43-04
dc.subject.classification 4.43-05
dc.subject.ddc 004
dc.title Object of Fixation Dataset en_US
dc.type Dataset en_US
dc.type Text en_US
dc.type Software en_US
dc.type Image en_US
dcterms.accessRights openAccess
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
tuda.history.classification Version=2016-2020;407-04 Verkehrs- und Transportsysteme, Logistik, Intelligenter und automatisierter Verkehr;409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung;

Files

Original bundle

Now showing 1 - 1 of 1
NameDescriptionSizeFormat
iv2019_ObjectOfFixation.zip26.24 GBZIP-Archivdateien Download