Self-learning image geometrical distortion correction
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Solution Overview
Problem
Conventional methods for distortion correction in images captured by cameras with non-rectilinear lenses, such as fisheye lenses, require manual adjustment of parameters and are time-consuming, especially in large installations, as they necessitate manual segmentation of floor and wall areas within the image.
Innovation Solution
A self-learning method that identifies the movement of bottom portions of objects, like feet, to automatically determine the floor boundary and build a 3D model, allowing cameras to learn and correct distortion without manual input, by assuming areas not involved in object movement are walls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of parameters is used for distortion correction, then the correction accuracy can be improved, but the installation time and labor cost increase significantly
Solution Approach 1:
The camera system automatically performs distortion correction by capturing images of a calibration pattern (such as a chessboard or circular grid), detecting the pattern features, and computing correction parameters without human intervention. This self-calibration process eliminates manual parameter adjustment while achieving accurate distortion correction.
Solution Approach 2:
The system performs distortion correction parameters calculation and application in advance during the installation phase by capturing calibration images and processing them automatically. This preliminary calibration ensures accurate distortion correction is ready before the camera is put into operational use.
2Measurement precision
If manual segmentation of floor and wall areas is required, then the 3D model accuracy can be improved, but the complexity of operation increases
Solution Approach 1:
The system replaces manual visual segmentation with automated computer vision algorithms that detect edges, lines, and geometric features in the captured images. These algorithms automatically identify floor and wall boundaries by analyzing pixel intensity gradients and geometric patterns, eliminating the need for manual area segmentation while maintaining accurate 3D model construction.
Solution Approach 2:
The camera system automatically performs feature detection and 3D model construction by processing captured images through algorithms that identify spatial relationships between detected features. The system self-calculates room dimensions, wall positions, and floor boundaries without requiring operator intervention for segmentation.
3Area of stationary object
If multiple cameras are installed, then the coverage area increases, but the total calibration time increases proportionally
Solution Approach 1:
Each camera in the multi-camera system independently performs automatic distortion correction and 3D model construction by capturing and processing its own calibration images. This parallel self-calibration approach allows multiple cameras to be calibrated simultaneously rather than sequentially, reducing total calibration time while maintaining comprehensive coverage area.
Solution Approach 2:
The system performs preliminary automatic calibration for all cameras during the installation phase by capturing calibration patterns and processing images in parallel. This batch calibration approach prepares all cameras for operation simultaneously, avoiding the time-consuming sequential manual calibration that would be required for multiple devices.
Data Source
Figure 1A~1B
Figure 1C~1G
Figure 1H~2A
AI summary
A method (200) of distortion correction in an image captured by a non-rectilinear camera (210) is provided, including obtaining multiple images (212) of a scene captured by the camera over time, determining (220) where bottom portions (222) of objects having moved over a horizontal surface in the scene are located in the images, determining (230) a boundary (232) of the horizontal surface in the scene based on the determined locations of the bottom portions, generating (240) a three-dimensional model (242) of the scene by defining one or more vertical surfaces around the determined boundary of the horizontal surface of the scene, and correcting (250) a distortion of at least one of the images by projecting the image onto the three-dimensional model of the scene. A corresponding device, computer program and computer program product are also provided.