Abnormality Detection via Image Segmentation and ROI Analysis
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Solution Overview
Problem
Existing abnormality detection systems that rely on video data struggle to accurately identify changes over time, particularly in environments where pixel values fluctuate due to natural changes or noise, leading to false alarms and reduced detection efficiency.
Innovation Solution
An abnormality detection apparatus that extracts target and reference images from video data, compares them based on pixel value differences, and generates alerts when significant changes are detected, using a masked image generation process to isolate object areas and reduce noise, thereby improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pixel value changes in video data are used to detect abnormalities, then detection capability is provided, but false alarms increase due to natural changes and noise
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROI) and processes each region separately. By dividing the image into multiple ROIs and analyzing pixel value changes in each region independently, the system can distinguish between significant abnormalities and natural variations, reducing false alarms while maintaining detection accuracy.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their local characteristics. Each ROI is analyzed with customized thresholds and parameters suited to its specific content, allowing the system to accurately detect abnormalities in each local area while adapting to natural variations in different regions.
2Speed
If video data is continuously monitored for pixel value changes, then real-time detection is achieved, but processing time and computational load increase
Solution Approach 1:
By segmenting the image into multiple ROIs, the system can process only the relevant portions of the image rather than the entire frame. This selective processing reduces computational load while maintaining real-time detection capability, as the system only analyzes regions where abnormalities are likely to occur.
Solution Approach 2:
The patent applies partial action by analyzing only specific regions and applying selective thresholding rather than processing the entire image uniformly. This approach reduces processing time and computational resources while maintaining effective abnormality detection in the most critical areas.
3Reliability
If the entire image is processed for pixel value changes, then comprehensive monitoring is achieved, but detection precision decreases due to noise and non-significant changes
Solution Approach 1:
The patent divides the image into multiple ROIs and analyzes each region separately with customized parameters. This segmentation allows the system to maintain comprehensive monitoring coverage across the entire image while improving detection precision in each local area by applying region-specific analysis rather than uniform processing.
Solution Approach 2:
The system applies local quality by tailoring the detection parameters and thresholds to the specific characteristics of each ROI. This allows comprehensive monitoring of the entire image while maintaining high precision in abnormality detection, as each region is analyzed with parameters optimized for its local content and characteristics.
Data Source
AI summary
An abnormality detection apparatus, an abnormality detection method, and an abnormality detection program are provided. Each of the abnormality detection apparatus, an abnormality detection method, and an abnormality detection program extracts a target image to be monitored and a reference image, respectively, from target video to be monitored, detects an abnormality based on a difference between the target image to be monitored and the reference image, and displays an image indicating a difference between the target image to be monitored and the reference image on a monitor. Moreover, an abnormality detection system is provided including the abnormality detection apparatus, a video that captures an image of a target to be monitored, and a monitor.