Visualization of Anything of Interest Below Water Surface

When conditions prevent visual scanning of environments and providing situational awareness using vision-based technologies, there need to be alternatives. The goal of this research is to provide users (decision makers such as operators of any boat or underwater craft) an accurate visualization of the real-time conditions of an operating (moving) machine (e.g., boat, submarine) when the visibility prevents reliable use of vision-based sensors.

Although identified/scored as a marine industry specific project, the scope of this topic is very broad and results could be applied in many industries. The topic spans everything from the use of non-visual sensors (e.g., sonar) for depth to the development of new computer-human interfaces for 3D data visualization.

Stakeholders

Developers, operators and owners/users of commercial, private or military water craft

Possible Methodologies

A research platform would be designed of lightweight and power efficient sensors that are effective as alternatives to cameras and existing vision-based environmental capture. The research would test and develop 3D interaction modes when a user’s natural vision is impaired.

Research Program

This topic or theme of research overlap with the topic of visualizing conditions in the direction or on the path of any moving object. The outcomes of this research could also be applied in non-industrial use cases (e.g., scuba diving or other sports in low visibility conditions). The same research topic could be combined with study of user interfaces and interaction paradigms for the visually-impaired community.

Miscellaneous Notes

None

Keywords

Situational awareness, night vision, non-visual scanning in real time, marine, rescue, emergency response, autonomous underwater vehicles, marine navigation, marine radar

Research Agenda Categories

Industries, Technology

Expected Impact Timeframe

Medium

Related Publications

Using the words in this topic description and Natural Language Processing analysis of publications in the AREA FindAR database, the references below have the highest number of matches with this topic:

• Ciocoiu, T. I., & Moldoveanu, F. D. (2017). Vision based localization stereo rig calibration and rectification. 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP).
• He, Y., & Chen, S. (2019). Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera. IEEE Access, 7, 12495-12510.
• Zhao, Y., Kupferstein, E., Castro, B. V., Feiner, S., & Azenkot, S. (2019). Designing AR Visualizations to Facilitate Stair Navigation for People with Low Vision. Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology.
• Park, Y. J., Ro, H., Byun, J.-H., & Han, T.-D. (2019). Adaptive projection augmented reality with object recognition based on deep learning. Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion.
• Azenkot, S., & Zhao, Y. (2017). Designing smartglasses applications for people with low vision. ACM SIGACCESS Accessibility and Computing, (119), 19-24.

More publications can be explored using the AREA FindAR research tool.

Author

Christine Perey

Last Published (yyyy-mm-dd)

2021-08-31

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