Vehicle Image Stitching via Pixel Clustering and Confidence Scoring
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Solution Overview
Problem
Traditional image stitching methods for autonomous vehicles require a high number of cameras to achieve a 360° field of view, leading to increased hardware and power consumption, as well as inefficient image processing due to high overlapping fields of view.
Innovation Solution
A method and system that uses an Electronic Control Unit (ECU) to segment and align images captured by fewer cameras by identifying pixel clusters, determining centroids, generating confidence scores, and adjusting camera positions based on score differences to minimize misalignment, thereby reducing the number of cameras needed for stitching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional high overlapping fields of view are used for image stitching, then the disconnected images can be recognized and stitched together, but the number of cameras required increases and processing complexity increases
Solution Approach 1:
The patent changes the parameter of overlap requirement from high (traditional) to minimal (invention). By using pixel clustering and confidence scoring, the system can achieve reliable stitching with minimal overlap between camera fields of view, reducing the number of cameras needed while maintaining stitching accuracy
Solution Approach 2:
The patent replaces the mechanical approach of using more cameras with high overlap (physical system) with an algorithmic approach using pixel clustering and confidence scoring (information processing system). This substitution allows achieving the same stitching reliability with fewer cameras by using computational methods instead of physical redundancy
2Reliability
If traditional high overlapping fields of view are used for image stitching, then the disconnected images can be recognized and stitched together, but the processing of image data increases
Solution Approach 1:
The patent segments the image matching process into distinct stages: pixel clustering based on characteristics, centroid determination for each cluster, and confidence score generation. This segmentation allows efficient processing by breaking down the complex stitching task into manageable computational steps that can be executed systematically
Solution Approach 2:
The patent creates a confidence score as a computational representation (copy) of the alignment quality between images. This confidence score serves as a proxy metric that enables efficient evaluation of stitching quality without requiring extensive manual verification or complex processing, thus improving productivity while maintaining reliability
3Area of stationary object
If more cameras are used to capture 360° view with minimal overlap, then the field of view coverage is maintained, but the hardware and power consumption increases
Solution Approach 1:
The patent changes the parameter of camera overlap from high to minimal, and compensates by using advanced image processing algorithms (pixel clustering, centroid determination, confidence scoring). This parameter change allows maintaining full 360° field of view coverage with fewer cameras, thereby reducing hardware quantity and power consumption while preserving the required area coverage
Data Source
AI summary
The present disclosure relates to a method of stitching images captured by a vehicle. A first image and a second image are received. The first image and the second image are segmented based on characteristics of pixels. Groups of pixels having similar characteristics are identified to form clusters in a predetermined portion of overlap of the first image and the second image. A confidence score is generated for the first image and the second image. A difference in the confidence score is computed. At least one of, the first image capturing unit and the second image capturing unit is aligned to capture at least one of, a first aligned image and a second aligned image based on the difference in the confidence score. The first aligned image and the second aligned image are stitched.