Abstract

Recognizing slanted deck scenes is crucial to security monitoring for protecting ships and making them behave smartly resilient. However, there are multitude of diverse structures that are designed as slanted planes due to rough maritime environments. Traditional methods for scene understanding from 3D point clouds or RGB-D data are energy-consuming and memory intensive, which makes those models less reliable in a resource-constrained system of limited compute, memory, and energy resources on ships. In this study, we present an approach to understanding deck scenes, including slanted structures, using a low-cost monocular camera without prior training. New clusters of slanted angle projections are extracted. The vanishing points of slanted non-Manhattan angle projections are estimated. These slanted planes can be reshaped by compositions of non-Manhattan angle projections. Combined with Manhattan planes, a deck scene can be approximated by Manhattan and non-Manhattan planes. Unlike deep learning-based algorithms, this approach requires no prior training or knowledge of the camera’s internal parameters. Experimental results demonstrated that the method can successfully elucidate diverse elements, including slanted structures, meeting safety monitoring requirements using a resource-constrained monocular camera in a deck environment.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.