Camera Calibration via Object Detection Bounding Boxes
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Solution Overview
Problem
Current camera calibration methods fail to accurately account for the errors introduced by the location of the camera, leading to inconsistencies between captured data and the real world, which affects the precision and accuracy of image or video generation.
Innovation Solution
A computer program that uses an object detection model to input images, acquire bounding box information, and perform calibration on image acquisition devices, including 3D bounding box information to correct for rotation angles and distances, thereby improving calibration accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then the calibration process is simple, but the calibration precision is insufficient due to camera location errors
Solution Approach 1:
The patent transitions from 2D image coordinates to 3D real-world coordinates by introducing depth information and spatial transformation. The calibration process incorporates three-dimensional bounding box information and performs coordinate system transformations between image space and real-world space, adding dimensional complexity to resolve the precision issue.
Solution Approach 2:
The patent introduces an object detection model as an intermediary component that generates bounding box information. This intermediary process transforms raw image data into structured calibration data, enabling more precise calibration while managing complexity through modular processing steps.
2Measurement precision
If 3D bounding box information is used for calibration, then calibration accuracy improves, but the computational complexity increases
Solution Approach 1:
The object detection model performs preliminary processing to generate 3D bounding box information before the calibration step. By pre-computing spatial relationships and object parameters, the system prepares calibration data in advance, reducing the computational burden during the actual calibration process.
Solution Approach 2:
The calibration process is segmented into distinct stages: object detection, bounding box information extraction, coordinate transformation, and parameter calibration. This segmentation allows computational tasks to be distributed and optimized independently, managing overall computational complexity.
3Measurement precision
If coordinate system transformation is performed, then the accuracy of object detection improves, but the processing time increases
Solution Approach 1:
The patent replaces manual or iterative coordinate transformation methods with a standardized mathematical transformation model. By establishing fixed transformation relationships between coordinate systems, the system reduces computational iterations and processing time while maintaining accuracy.
Data Source
AI summary
According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program causes one or more processors to perform operations below for performing calibration when the one or more processors are executed, and the operations may include: an operation of inputting an image to an object detection model and performing object detection by using the object detection model; an operation of acquiring bounding box information on the detected object; and an operation of performing calibration on an image acquisition device based on at least a part of the bounding box information.