Multi-Camera Parameter Configuration for Uniform Image Appearance
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Solution Overview
Problem
In modern security systems with multiple surveillance cameras, achieving uniform image appearance across different cameras is challenging due to varying light conditions and camera types, making it difficult for operators to monitor and analyze images effectively.
Innovation Solution
A method and apparatus for configuring parameter values for multiple cameras connected via a data communications network, allowing for image feedback after reconfiguration, which includes acquiring and buffering image data, changing parameter values, and displaying updated images to ensure consistency across cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If predetermined settings are used for multiple cameras, then configuration time is reduced, but image appearance uniformity deteriorates due to different light conditions and camera types
Solution Approach 1:
The system automatically performs preliminary image analysis and parameter optimization before final configuration. It pre-processes images from multiple cameras, analyzes their characteristics, and prepares optimized parameter sets that account for different light conditions and camera types, thereby achieving both time efficiency and uniformity.
Solution Approach 2:
The system dynamically adjusts multiple image parameters (exposure, gain, white balance, sharpness, noise reduction) based on analyzed image characteristics. By changing these parameters adaptively for each camera while maintaining overall uniformity through centralized control, the system resolves the contradiction between quick configuration and appearance consistency.
2Manufacturing precision
If manual adjustment of parameter values is performed for each camera, then image appearance uniformity is improved, but configuration time and complexity increase
Solution Approach 1:
The system enables self-service configuration by automatically analyzing images from each camera and determining optimal parameter values without requiring manual intervention. The cameras and central processor work autonomously to achieve uniform appearance, eliminating time-consuming manual adjustments while maintaining precision.
Solution Approach 2:
The system implements feedback mechanisms where image quality metrics are continuously monitored and used to adjust parameters. The central processor receives image data, analyzes uniformity across cameras, and iteratively refines parameter settings until desired appearance consistency is achieved, reducing configuration time through automated feedback loops.
3Loss of time
If automated parameter configuration is implemented, then configuration time is reduced, but ability to handle different light conditions and camera types deteriorates
Solution Approach 1:
The system applies local quality adjustments by analyzing each camera's specific characteristics and light conditions independently. It determines camera-specific parameters tailored to local conditions (individual light environments, camera types, and positions) while maintaining global uniformity through centralized coordination, thereby achieving both speed and adaptability.
Solution Approach 2:
The system performs comprehensive parameter changes across multiple dimensions (exposure, gain, white balance, sharpness, noise reduction) to adapt to different light conditions and camera types. By dynamically adjusting these parameters based on automated analysis of each camera's operating conditions, the system maintains high adaptability while reducing configuration time.
Data Source
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AI summary
A method for configuring parameter values for a plurality of cameras (100a-100f) is disclosed. In the first step of the method, image data from the plurality of cameras (100a-100f) is acquired. Secondly, the image data from the plurality of cameras (100a-100f) is buffered. Thirdly, the buffered image data from the plurality of cameras (100a-100f) is displayed. Fourthly, at least one parameter value for a first subset of said plurality of cameras is changed. Fifthly, the at least one parameter value is transmitted to said first subset of cameras. Sixthly, the changed image data from a second subset of said plurality of cameras is acquired. Seventhly, the buffered image data for said second subset of cameras is replaced with said changed image data for said second subset of cameras. Finally, the stored image data for said plurality of cameras (100a-100f) is displayed.