Dynamic Background Update for Outdoor Surveillance
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Solution Overview
Problem
Conventional image processing methods for surveillance cameras, particularly outdoors, face challenges in accurately detecting objects due to rapid light changes from weather conditions, leading to false alarms and low detection accuracy, as they often update the background image with moving objects, causing incorrect object detection.
Innovation Solution
An image processing apparatus and method that dynamically updates the background image and threshold values based on the difference between the current and previous frames, adjusting the background by incorporating the latest image in low-difference scenarios and recalculating thresholds in high-difference scenarios to accurately distinguish between background and foreground.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the background image is updated with the latest image when light intensity changes, then the background image adapts to new lighting conditions, but moving objects in the latest image are incorrectly incorporated into the background causing false detection
Solution Approach 1:
The patent segments the image into multiple regions (first region with larger average brightness, second region with smaller average brightness) and processes each region independently with different update rules. This segmentation allows the system to adapt to lighting changes in specific regions without allowing moving objects in other regions to contaminate the background image.
Solution Approach 2:
Different update strategies are applied to different regions of the background image based on their local characteristics (brightness levels). The first region (brighter) uses one update rule while the second region (darker) uses another update rule, allowing local adaptation to lighting conditions while maintaining global detection accuracy.
2Measurement precision
If the background image is not updated, then false detection is reduced, but the system cannot adapt to rapid light changes from weather conditions
Solution Approach 1:
The patent implements dynamic background image updating where the update frequency and strategy change based on current lighting conditions. When light intensity changes are detected (indicating weather changes), the system dynamically adjusts to update the background more frequently, while maintaining strict object detection to prevent false positives during transition periods.
Solution Approach 2:
The system performs periodic background image updates at specific intervals or triggers (such as when accumulated brightness difference exceeds a threshold). This periodic action allows the system to adapt to gradual lighting changes while maintaining stability during normal operating conditions.
3Reliability
If manual surveillance is used, then false alarms are reduced, but manpower requirements increase and detection reliability decreases due to human error
Solution Approach 1:
The patent implements feedback mechanisms where detection results and background image quality are continuously monitored. When false detections are detected or lighting conditions change significantly, the system adjusts its behavior (such as pausing background updates or adjusting update thresholds) to maintain reliable operation, creating a self-correcting system that reduces false alarms while maintaining automation.
Data Source
AI summary
A background image holding unit is configured to hold a background image. A threshold table holding unit is configured to hold a threshold. A previous frame image holding unit is configured to hold a previous frame image. An object extraction unit is configured to perform a background subtraction process of calculating difference values between the background image and a latest image and detecting a pixel whose calculated difference value is equal to or larger than the threshold as an extracted object, and configured to perform a process of judging the magnitude of difference between the background image and latest image. The object extraction unit is configured to update the background image and threshold in accordance with the magnitude of difference between the background image and latest image.