Camera System Image Recognition Using Environment Parameter Tables
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Solution Overview
Problem
Existing image recognition systems face challenges in maintaining performance when the actual imaging environment differs from the assumed general environmental conditions, requiring frequent recalibration and additional learning, which can be resource-intensive and inefficient, especially in large-scale camera systems.
Innovation Solution
A method for image recognition using a camera system that acquires imaging environment information and uses a parameter table to determine a recognition control parameter based on similarity with pre-stored parameters, allowing for adaptive recognition without constant recalibration and reducing the need for extensive additional learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the detector is trained through machine learning for general environmental conditions, then the recognition accuracy is improved under those conditions, but the performance deteriorates when the actual imaging environment differs from the assumed conditions
Solution Approach 1:
The patent changes the parameters of the recognition system by introducing recognition control parameters that adjust the detector's behavior based on imaging environment information. Instead of retraining the entire detector for each environment, the system modifies operational parameters (such as threshold values, feature weights, or detection sensitivity) to adapt to different lighting, temperature, and scene conditions, thereby maintaining high recognition accuracy across varying environments without extensive retraining
Solution Approach 2:
The patent performs preliminary action by pre-collecting imaging environment information and pre-determining appropriate recognition control parameters for various environmental conditions. A parameter table is created in advance that maps environmental conditions to optimized recognition parameters, allowing the system to quickly adapt to new environments by looking up pre-computed parameters rather than performing real-time optimization or retraining
2Measurement precision
If frequent recalibration and additional learning are performed to maintain performance in varying environments, then the recognition accuracy is maintained, but the system complexity and resource consumption increase
Solution Approach 1:
The patent uses copying by creating a parameter table that stores pre-determined recognition control parameters for different imaging environments. Instead of performing complex recalibration operations each time the environment changes, the system copies the appropriate pre-stored parameters from the table based on the current environmental conditions, significantly reducing computational overhead and system complexity while maintaining recognition accuracy
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing optimization parameters for various environmental conditions before actual deployment. This preliminary preparation eliminates the need for frequent real-time recalibration, reducing both system complexity and resource consumption during operation while maintaining high recognition performance
3Measurement precision
If extensive additional learning is performed to adapt to specific imaging environments, then the recognition accuracy for that environment is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-determining recognition control parameters for specific imaging environments before actual recognition tasks. The parameter table is populated in advance with environment-specific optimizations, allowing the system to instantly apply the correct parameters when an environment is detected, eliminating the need for time-consuming additional learning or real-time optimization during operation
Data Source
AI summary
A first image taken by a first camera device in the plurality of camera devices and first imaging environment information indicating a first imaging environment of the first camera device at a time of taking the first image is acquired. By using a parameter table that manages imaging environment information indicating an imaging environment at a time of taking an image previously by a camera device and a recognition control parameter indicating a detector corresponding to an imaging environment, a first recognition control parameter indicating a first detector corresponding to third imaging environment that is identical or similar to the first imaging environment indicated by the first imaging environment information acquired from the first camera device is selected from the recognition control parameters. The first image acquired from the first camera device is recognized by using the first detector indicated by the selected first recognition control parameter.