Automatic Vehicle Camera Configuration Through Field-of-View Analysis
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Solution Overview
Problem
Conventional camera configuration for vehicles relies on manual determination, which is costly, time-consuming, and prone to errors.
Innovation Solution
A system and method that utilizes a camera and a processor to autonomously set vehicle configuration parameters based on imaging information within the camera's field of view, including location, orientation, and settings like frame rate and resolution, using video analytics and neural networks for accurate configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration is used to determine camera parameters, then configuration accuracy can be achieved, but the process becomes costly and time-consuming
Solution Approach 1:
The camera system performs self-configuration by automatically capturing images, analyzing the field of view, and determining optimal parameters without human intervention. The processor autonomously processes the captured images to identify objects, scenes, and optimal camera settings, eliminating the need for manual installer configuration while maintaining high accuracy.
Solution Approach 2:
The manual mechanical process of installer configuration is replaced with an automated electronic system using image processing algorithms and neural networks. The system substitutes human expertise with computational analysis that automatically determines camera parameters based on visual data from the captured field of view.
2Reliability
If manual configuration is used to set camera parameters, then proper setup can be achieved, but error rates increase
Solution Approach 1:
The autonomous configuration system eliminates human error by performing self-configuration through automated image analysis. The processor objectively evaluates the field of view and determines parameters based on algorithmic analysis rather than subjective human judgment, significantly reducing configuration errors and improving reliability.
Solution Approach 2:
The system captures test images, analyzes them through processing algorithms, and uses the analysis results to automatically adjust and finalize camera parameters. This feedback loop ensures that the configuration is optimized based on actual visual data from the installation location, improving reliability by verifying settings before final deployment.
3Productivity
If automated configuration is implemented, then speed and efficiency improve, but system complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: capturing test images, analyzing the field of view, identifying objects and scenes, and determining optimal camera parameters all in one integrated system. This multi-functionality achieves fast automated configuration without requiring separate complex subsystems for each task.
Solution Approach 2:
The system uses captured images as an intermediary medium between the camera hardware and the configuration parameters. The images serve as the basis for automated analysis, allowing the system to determine optimal settings without direct human intervention while keeping the overall architecture relatively simple and modular.
Data Source
AI summary
A system includes a camera and at least one processor. The camera has a field of view, and is configured to acquire imaging information within the field of view. The at least one processor is operably coupled to the camera and configured to acquire the imaging information from the camera. The at least one processor is configured to autonomously set vehicle configuration parameters for use of the camera with a vehicle based on the imaging information within the field of view.