Fine-Grained Visual Recognition in Mobile Augmented Reality for Technical Support

Augmented Reality is increasingly explored as the new medium for two-way remote collaboration applications to guide the participants more effectively and efficiently via visual instructions. As users strive for more natural interaction and automation in augmented reality applications, new visual rec...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 26; H. 12; S. 3514 - 3523
Hauptverfasser: Zhou, Bing, Guven, Sinem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applications programs
Augmented reality
Cameras
Collaboration
Image recognition
Maintenance engineering
mobile
Mobile computing
Object recognition
Task complexity
Technical services
Three-dimensional displays
Visual recognition
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Zusammenfassung:Augmented Reality is increasingly explored as the new medium for two-way remote collaboration applications to guide the participants more effectively and efficiently via visual instructions. As users strive for more natural interaction and automation in augmented reality applications, new visual recognition techniques are needed to enhance the user experience. Although simple object recognition is often used in augmented reality towards this goal, most collaboration tasks are too complex for such recognition algorithms to suffice. In this paper, we propose a fine-grained visual recognition approach for mobile augmented reality, which leverages RGB video frames and sparse depth feature points identified in real-time, as well as camera pose data to detect various visual states of an object. We demonstrate the value of our approach through a mobile application designed for hardware support, which automatically detects the state of an object to present the right set of information in the right context.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2020.3023635