Vehicle Camera Calibration via Automatic Region of Interest Detection
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Solution Overview
Problem
Existing vehicle camera calibration methods require manual user input to define a region of interest, which can be inefficient and prone to errors due to variable mounting orientations and positions, especially for aftermarket cameras.
Innovation Solution
An apparatus comprising a camera and a processing unit that identifies objects in front of the vehicle using a neural network model, determines their distribution, and automatically calculates a region of interest based on this distribution, allowing for automatic calibration and periodic updates without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input is required to define region of interest during calibration, then calibration accuracy can be improved, but calibration efficiency and ease of operation deteriorate
Solution Approach 1:
The system performs self-calibration by automatically identifying objects in the camera field of view and determining the region of interest without requiring manual user input. The processing unit analyzes captured images, identifies objects such as vehicles or pedestrians, and automatically defines the ROI based on object distribution, enabling the system to calibrate itself independently.
Solution Approach 2:
The system captures multiple images of the environment before final calibration is needed. By pre-identifying objects in these images and determining their distribution patterns, the system prepares calibration data in advance, allowing automatic ROI definition to occur quickly when calibration is triggered without requiring real-time manual intervention.
2Measurement precision
If manual user input is required to define region of interest during calibration, then calibration precision can be improved, but productivity deteriorates
Solution Approach 1:
The processing unit automatically performs object identification and ROI determination through algorithmic analysis of captured images. The system uses computer vision techniques to detect objects, calculate their spatial distribution, and define the region of interest automatically, eliminating the need for manual calibration operations and significantly improving calibration productivity.
Solution Approach 2:
The manual mechanical process of placing markers or drawing ROIs by hand is replaced with an automated computer vision system. The processing unit uses image processing algorithms to automatically identify objects and calculate their distribution patterns, substituting human manual operations with automated computational methods that are both precise and efficient.
3Adaptability or versatility
If cameras are mounted at different angles and positions, then adaptability improves, but calibration complexity increases
Solution Approach 1:
The calibration system is designed to dynamically adapt to different camera mounting positions and angles. Rather than requiring fixed installation parameters, the system automatically detects objects in the actual camera field of view and determines the ROI based on the observed object distribution, allowing the calibration process to adjust dynamically to any mounting configuration.
Solution Approach 2:
The system changes its calibration approach based on the actual mounting parameters. Instead of using predetermined calibration parameters, the processing unit analyzes the captured images to determine object distribution patterns and automatically adjusts the ROI definition according to the specific mounting position and orientation, effectively adapting to parameter variations without increasing complexity.
4Reliability
If the entire camera image is processed, then detection completeness improves, but computation time increases
Solution Approach 1:
The system extracts only the relevant portion of the camera image for processing by automatically determining a region of interest based on object distribution. By identifying objects in the full image first and then extracting the specific ROI that contains these objects, the system processes only the necessary subset of the image data, reducing computation time while maintaining detection completeness for relevant objects.
Solution Approach 2:
The system performs preliminary object identification across the entire image to determine the region of interest before final processing. By pre-analyzing the full image to locate objects and calculate their distribution, the system identifies which portions of the image are relevant, allowing subsequent processing to focus only on the extracted ROI rather than the entire image, thus reducing computation time.
Data Source
AI summary
An apparatus includes: a first camera configured to view an environment outside a vehicle; and a processing unit configured to receive images generated at different respective times by the first camera; wherein the processing unit is configured to identify objects in front of the vehicle based on the respective images generated at the different respective times, determine a distribution of the identified objects, and determine a region of interest based on the distribution of the identified objects in the respective images generated at the different respective times.