Image Processing Apparatus for Distinguishing Subject Movement from Illumination Changes
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Solution Overview
Problem
Existing methods for analyzing video images struggle to distinguish between subject movement and illumination changes, leading to false alarms in health and security monitoring, as global solutions fail to account for varying illumination conditions and artefacts.
Innovation Solution
A method involving the selection of two image frames, division into cells, calculation of spatial frequency components, and determination of differences to differentiate between subject movement and illumination changes by setting thresholds based on the number of cells and components with significant magnitude differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If global illumination analysis is used across the whole image frame, then illumination changes can be detected, but false alarms occur when distinguishing subject movement from illumination artefacts
Solution Approach 1:
The image frame is divided into multiple local regions or blocks, and illumination analysis is performed independently for each region. This allows the system to detect illumination changes locally without mistaking them for subject movement, thereby reducing false alarms while maintaining detection accuracy.
Solution Approach 2:
Different analysis methods are applied to different parts of the image based on local characteristics. Regions with significant illumination changes are handled differently from regions with stable illumination, allowing precise distinction between illumination artefacts and actual subject movement.
2Productivity
If simple video analytics methods are used to detect movement, then processing speed is maintained, but illumination changes are mistaken for gross subject movement
Solution Approach 1:
By segmenting the image into local regions and analyzing spatial frequency components independently in each region, the system can efficiently distinguish between illumination changes and subject movement without requiring complex global analysis, thus maintaining processing speed while improving accuracy.
Solution Approach 2:
The system analyzes spatial frequency components rather than simple pixel intensity differences. This parameter transformation allows the system to differentiate between low-frequency illumination changes and high-frequency subject movement patterns, improving detection accuracy while keeping computational complexity manageable.
Data Source
AI summary
In order to detect gross subject movement in a video image in a way which is not sensitive to illumination change, for example illumination changes caused by movement of shadows or sunlight, spaced pairs of image frames are selected from a video sequence and sub-divided into cells, and spatial frequency analysis is performed in each cell. The magnitude of the spatial frequency components in corresponding cells in the two selected image frames are compared. If the number of cells with high magnitude difference is high then the video image is determined as containing gross subject movement whereas if the number of cells with high magnitude differences is low, the sequence is determined as not containing gross movement, though it may contain illumination changes or no or fine movement.