Video Image Recognition Apparatus Illumination Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video image recognition techniques face challenges in accurately detecting persons and recognizing movements due to illumination variations, leading to decreased performance and incorrect detections, especially in environments with changing light conditions.
Innovation Solution
An apparatus with an analysis unit, recognition unit, and learning unit that analyzes environmental conditions and adjusts video image parameters, such as exposure and focus, to autonomously learn and adapt the classifier, reducing the need for a large-scale database and improving recognition accuracy across varying illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If re-learning of the classifier is performed using only initial classifier without illumination variation analysis, then device complexity is reduced, but video image recognition accuracy deteriorates under illumination variations
Solution Approach 1:
The system performs preliminary analysis of illumination variations in the environment before performing classifier re-learning. The analysis unit analyzes the state of the environment at the time when the capturing unit captures video images, based on parameters for correcting the captured video images. This preliminary action allows the system to prepare appropriate correction parameters in advance, ensuring accurate recognition even when illumination conditions change, without requiring overly complex learning mechanisms.
Solution Approach 2:
The system introduces feedback by analyzing the results of video image recognition and using this feedback to determine whether classifier re-learning is necessary. The analysis unit evaluates the captured video images and provides feedback on illumination conditions, which then guides the learning unit to perform re-learning only when needed. This feedback mechanism maintains recognition accuracy while avoiding unnecessary learning operations that would increase device complexity.
2Measurement precision
If classifier re-learning is performed frequently to adapt to illumination variations, then video image recognition accuracy is improved, but loss of time increases due to repeated learning operations
Solution Approach 1:
The system performs preliminary analysis of illumination variations before triggering classifier re-learning. By analyzing environmental parameters and illumination conditions in advance, the system can determine whether re-learning is actually necessary. This prevents unnecessary re-learning operations from being performed when illumination conditions are within acceptable ranges, thereby reducing time loss while maintaining recognition accuracy when it is truly needed.
Solution Approach 2:
The system uses the analysis unit to automatically evaluate illumination conditions and determine whether classifier re-learning is required, without requiring external intervention or continuous monitoring. The learning unit performs re-learning autonomously based on the analysis results, serving itself by adapting to illumination variations only when necessary. This self-service approach minimizes time loss by avoiding unnecessary learning operations while maintaining recognition accuracy.
3Measurement precision
If a large-scale database is used to train the classifier for various illumination conditions, then video image recognition accuracy is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential illumination variation parameters from the video images that are necessary for accurate recognition. Instead of using a large-scale database containing diverse illumination conditions, the analysis unit extracts key parameters for correcting the captured video images based on the actual environmental illumination state. This extraction approach maintains recognition accuracy while avoiding the complexity of managing and processing large databases.
Solution Approach 2:
The system changes parameters for correcting the captured video images based on analyzed illumination conditions rather than relying on a large-scale database. The learning unit adjusts classifier parameters dynamically according to the environmental state analyzed by the analysis unit. This parameter change approach allows the system to adapt to different illumination conditions without requiring extensive training data, thereby reducing device complexity while maintaining recognition accuracy.
Data Source
AI summary
An apparatus includes an analysis unit configured to analyze a state of an environment at a time when a capturing unit captures a video image, based on a parameter for correcting the captured video image, a recognition unit configured to perform recognition processing on the captured video image, using a classifier, and a learning unit configured to learn the classifier based on a result of the analysis performed by the analysis unit and a result of the recognition performed by the recognition unit.