Abstract
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 orientation of near-symmetrical objects matters. 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 through a user study, comparing our tracking-based instructions to other methods, confirming the benefits of our approach in terms of preference and lower
Original language | English |
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Title of host publication | Proceedings of the ACM on Human-Computer Interaction |
Editors | Ville Mäkelä, Andrés Lucero, Florian Alt, Mark Hancock |
Number of pages | 20 |
Place of publication | New York |
Publisher | Association for Computing Machinery |
Publication date | Nov 2023 |
Article number | 426 |
DOIs | |
Publication status | Published - Nov 2023 |
Event | ACM Interactive Surfaces and Spaces - Carnegie Mellon Hamerschlag Hall, Pittsburg, United States Duration: 5 Nov 2023 → 8 Nov 2023 https://iss2023.acm.org/ |
Conference
Conference | ACM Interactive Surfaces and Spaces |
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Location | Carnegie Mellon Hamerschlag Hall |
Country/Territory | United States |
City | Pittsburg |
Period | 05/11/2023 → 08/11/2023 |
Internet address |
Series | Proceedings of the ACM on Human-Computer Interaction |
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Number | ISS |
Volume | 7 |
ISSN | 2573-0142 |
Keywords
- dataset generation
- deep learning
- guidance
- industry 4.0
- near-symmetrical objects
- tracking
- user study