Computer Vision for Carriers: PATRIOT

• URL: http://arxiv.org/abs/2311.15914v1
• Date: Mon, 27 Nov 2023 15:23:25 GMT
• Title: Computer Vision for Carriers: PATRIOT
• Authors: Ari Goodman, Gurpreet Singh, James Hing, Ryan O'Shea
• Abstract summary: PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets. Software was tested with synthetic and real-world data and was able to accurately extract the pose of assets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deck tracking performed on carriers currently involves a team of sailors manually identifying aircraft and updating a digital user interface called the Ouija Board. Improvements to the deck tracking process would result in increased Sortie Generation Rates, and therefore applying automation is seen as a critical method to improve deck tracking. However, the requirements on a carrier ship do not allow for the installation of hardware-based location sensing technologies like Global Positioning System (GPS) sensors. PATRIOT (Panoramic Asset Tracking of Real-Time Information for the Ouija Tabletop) is a research effort and proposed solution to performing deck tracking with passive sensing and without the need for GPS sensors. PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets. PATRIOT would allow for faster, more accurate, and less laborious asset tracking for aircraft, people, and support equipment. PATRIOT is anticipated to benefit the warfighter by reducing cognitive workload, reducing manning requirements, collecting data to improve logistics, and enabling an automation gateway for future efforts to improve efficiency and safety. The authors have developed and tested algorithms to perform pose estimations of assets in real-time including OpenPifPaf, High-Resolution Network (HRNet), HigherHRNet (HHRNet), Faster R-CNN, and in-house developed encoder-decoder network. The software was tested with synthetic and real-world data and was able to accurately extract the pose of assets. Fusion, tracking, and real-world generality are planned to be improved to ensure a successful transition to the fleet.

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