Recognizing Slanted Deck Scenes by Non-Manhattan Spatial Right Angle Projection

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.

Authors

• Luping Wang
• Hui Wei National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, China Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China

Keywords

• Three-dimensional displays
• Layout
• Cameras
• Visualization
• Security
• Marine vehicles
• Solid modeling
• Surveillance
• 3D Reconstruction
• Point Cloud
• Semantic Information
• Geometric Features
• Monocular
• Prior Training
• Human Visual System
• 3D Point Cloud
• Depth Perception
• 3D Scene
• Scene Understanding
• Street Scenes
• Scene Reconstruction
• RGB-D Data
• Indoor Environments
• Domain Adaptation
• Deep Learning-based Methods
• Virtual Line
• slanted structure
• non-Manhattan
• interpretability
• deck environment