Fisheye Collage Transformation for Object Detection
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Solution Overview
Problem
Current object detection systems face challenges in achieving a 360° field of view using a single camera due to fisheye lens images' strong visual distortions, which require costly multi-camera systems or computationally expensive post-processing to correct, limiting resolution and scene coverage.
Innovation Solution
A fisheye collage transformation method that identifies regions of interest in fisheye images, applies transformations to warp and rotate them, and generates a collage image for object detection, allowing for efficient processing and improved field of view without the need for multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera with fisheye lens is used to achieve 360° field of view, then the device complexity is reduced, but the image quality deteriorates due to strong visual distortions
Solution Approach 1:
The fisheye image is divided into multiple regions of interest (ROIs), each representing different spatial zones. These ROIs are then independently transformed and collaged to create a composite image that covers a wider field of view while maintaining image quality in each segment.
Solution Approach 2:
A collage transformation process is introduced as an intermediary step between capturing the fisheye image and performing object detection. This transformation includes undistorting and rotating specific ROIs to produce a corrected collage image that eliminates visual distortions while preserving the wide field of view.
2Area of stationary object
If multi-camera system is used to cover wider scene, then the field of view is improved, but the device complexity and cost increase
Solution Approach 1:
Instead of using multiple physical cameras to capture different views, the system creates virtual copies of the fisheye image through collage transformation. Multiple regions from a single fisheye image are transformed and assembled to simulate the effect of multiple cameras, achieving the same wide coverage without the hardware complexity.
3Manufacturing precision
If fisheye image post-processing is applied to correct distortions, then the image quality is improved, but the computational cost increases
Solution Approach 1:
Instead of applying uniform post-processing to the entire fisheye image, the system identifies and processes only specific regions of interest that contain objects of interest. This localized approach applies undistortion and rotation transformations only where needed, reducing overall computational cost while maintaining image quality in critical areas.
4Measurement precision
If fisheye collage transformation is applied to regions of interest, then the object detection accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary identification of regions of interest in the fisheye image before applying the collage transformation. By pre-segmenting the image and identifying which regions contain objects of interest, the system prepares the data in advance, allowing for more efficient transformation and rotation operations that reduce overall processing time while maintaining detection accuracy.
Data Source
AI summary
A method includes obtaining a fisheye image of a scene and identifying multiple regions of interest in the fisheye image. The method also includes applying one or more transformations to transform and rotate one or more of the regions of interest in the fisheye image to produce one or more transformed regions. The method further includes generating a collage image having at least one portion based on the fisheye image and one or more portions containing the one or more transformed regions. In addition, the method includes performing object detection to identify one or more objects captured in the collage image.