Camera Image Adjustment via Event Presence Value Analysis
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Solution Overview
Problem
Current camera systems face challenges in optimizing image quality for specific monitoring situations, as they often require manual adjustments of camera settings and image processing parameters, which can be inefficient and resource-intensive.
Innovation Solution
A method that determines image adjustment parameters by analyzing a plurality of images to detect specific events, identify their locations, calculate presence values, and adjust camera settings or image processing based on these values, focusing on areas with high event occurrence probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustments of camera settings and image processing parameters are used, then image quality can be optimized for specific monitoring situations, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The system automatically determines image adjustment parameters by analyzing captured images and detecting events, eliminating the need for manual adjustment. The camera performs self-optimization by calculating presence values and selecting appropriate parameters based on detected events and their locations, making the system self-sufficient in maintaining optimal image quality.
Solution Approach 2:
The system dynamically changes image adjustment parameters such as exposure time, gain, and processing settings based on the detected events and their presence values. By automatically modifying these parameters according to the monitoring situation, the system optimizes image quality for different scenarios without manual intervention.
2Manufacturing precision
If comprehensive image processing is applied to all areas, then image quality is maximized, but resource usage increases unnecessarily
Solution Approach 1:
The system applies different processing quality levels to different locations based on event presence values. Areas with high event occurrence receive optimized processing, while areas with low presence values use reduced processing. This localized approach ensures image quality is maintained where needed while conserving computational resources in less critical areas.
Solution Approach 2:
Instead of applying full processing to all image areas, the system performs partial processing focused on regions with detected events. By concentrating processing resources on relevant areas identified through event detection and presence value calculation, the system achieves effective image quality optimization with reduced overall resource consumption.
3Adaptability or versatility
If camera settings are adjusted for all possible monitoring situations, then versatility is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts camera settings and processing parameters based on real-time event detection and analysis. Rather than pre-configuring multiple static settings for different situations, the system adapts its parameters automatically according to the current monitoring scenario, achieving versatility through dynamic response rather than complex pre-programming.
Solution Approach 2:
The system uses feedback from event detection and presence value calculation to automatically determine appropriate image adjustment parameters. By continuously monitoring the scene and adjusting settings based on detected events, the system achieves adaptability to various monitoring situations without requiring complex manual configuration or multiple pre-set modes.
Data Source
AI summary
The present invention relates to a method and a camera for determining an image adjustment parameter. The method includes receiving a plurality of images representing an image view, detecting from the plurality of images events of a specific event type, identifying a location within the image view where the event of the specific type is present, determining a presence value of each of the identified locations, and determining an image adjustment parameter based on data from an adjustment location within the image view. The adjustment location is determined based on the presence value in each location of a plurality of locations within the image view.