Texture Mapping for Real-Time Panoramic Video Stitching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating panoramic videos from multiple video feeds are inefficient due to high computational requirements, particularly for real-time processing and lens distortion correction in airport surveillance applications.
Innovation Solution
Employing texture mapping techniques to correct lens distortion and stitch video frames from multiple cameras, reducing computational complexity and enabling real-time panoramic video generation with lens distortion correction, field of view stitching, and color normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional computer vision algorithms are used for image stitching, then image stitching can be achieved, but substantial computer processing resources are required making it unsuitable for real-time video processing
Solution Approach 1:
The patent replaces conventional computer vision algorithms with a texture mapping approach that uses mathematical projection transformations. Instead of using complex image processing algorithms to find correspondences and stitch images, the system uses predefined 3D surface models and projects video frames onto these surfaces using texture mapping techniques, significantly reducing computational requirements while maintaining stitching quality
Solution Approach 2:
The system performs preliminary configuration by defining 3D surface models and their relationships before video processing begins. The 3D surfaces are pre-configured with vertices and mapping parameters, allowing video frames to be directly projected onto these pre-established structures without requiring real-time computation of geometric relationships
2Manufacturing precision
If lens distortion correction is performed using conventional methods, then distortion can be corrected, but the process is computationally intensive due to remapping every pixel and interpolating missing information
Solution Approach 1:
The patent replaces conventional pixel-by-pixel remapping and interpolation methods with a texture mapping approach that uses mathematical projection transformations. The system projects video frames onto pre-defined 3D surfaces using vertex-based transformations, which reduces the computational complexity from O(n²) pixel operations to O(n) vertex operations while maintaining correction accuracy
Solution Approach 2:
The patent segments the image correction process by working with 3D surface vertices and mesh structures rather than individual pixels. By defining correction transformations at the vertex level and interpolating across surface meshes, the system reduces computational complexity while maintaining correction accuracy across the entire image
3Area of stationary object
If multiple video cameras are used to cover extensive airport apron areas, then adequate coverage is achieved, but generating a single panoramic view from multiple feeds becomes computationally demanding
Solution Approach 1:
The patent transitions from 2D image processing to 3D surface modeling by defining video feeds as textures on 3D surfaces. This dimensional elevation allows the system to handle multiple camera feeds by projecting them onto a unified 3D coordinate system, simplifying the integration of extensive coverage areas while reducing computational complexity
Solution Approach 2:
The patent creates a universal 3D surface model that can accommodate multiple video feeds from different cameras with different fields of view. The same 3D surface framework and texture mapping approach works for any number of cameras covering any area, providing a scalable solution for extensive airport apron coverage
Data Source
AI summary
Panoramic videos are generated from multiple video feeds in real time received from multiple video cameras, such as an array of video cameras having overlapping fields of view. Texture mapping techniques are employed to correct lens distortion or other defects or deficiencies in the video frames in each of the video feeds caused by optical properties of the corresponding video camera. Video frames of related video feeds, such as the video feeds of cameras in an array of cameras having adjacent and overlapping fields of view, are seamlessly stitched automatically based on an initial manual configuration, again employing texture mapping techniques. A colour profile of the different video frames is normalized to provide a uniform and seamless colour profile of the video panoramic view.