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Media type:
Text;
E-Article
Title:
CADTrack : Instructions and Support for Orientation Disambiguation of Near-Symmetrical Objects
Contributor:
Evangelista Belo, João Marcelo
[Author];
Wissing, Jon
[Author];
Feuchtner, Tiare
[Author];
Grønbæk, Kaj
[Author]
Published:
KOPS - The Institutional Repository of the University of Konstanz, 2023
Published in:Proceedings of the ACM on Human-Computer Interaction. ACM. 2023, 7(ISS), 426. eISSN 2573-0142. Available under: doi:10.1145/3626462
Language:
English
DOI:
https://doi.org/10.1145/3626462
ISBN:
1869960386
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Determining the correct orientation of objects can be critical to succeed in tasks like assembly and quality assurance. In particular, near-symmetrical objects may require careful inspection of small visual features to disambiguate their orientation. We propose CADTrack, a digital assistant for providing instructions and support for tasks where the object orientation matters but may be hard to disambiguate with the naked eye. Additionally, we present a deep learning pipeline for tracking the orientation of near-symmetrical objects. In contrast to existing approaches, which require labeled datasets involving laborious data acquisition and annotation processes, CADTrack uses a digital model of the object to generate synthetic data and train a convolutional neural network. Furthermore, we extend the architecture of Mask R-CNN with a confidence prediction branch to avoid errors caused by misleading orientation guidance. We evaluate CADTrack in a user study, comparing our tracking-based instructions to other methods to confirm the benefits of our approach in terms of preference and required effort. ; published