Flicker Process for Automated Image Change Detection
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Solution Overview
Problem
Current image capturing devices struggle to accurately track moving objects in real-time, relying on subjective human perception for change detection, which limits their effectiveness in monitoring and security applications.
Innovation Solution
The Flicker Process, a monitoring support system that automatically compares baseline and comparison views over time, highlighting differences through visual indicators like flashing, color, or ghosting, allowing for objective change detection and recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image capturing devices are used to monitor moving objects, then the ability to capture images is improved, but the ability to accurately track and follow moving objects automatically deteriorates
Solution Approach 1:
The system continuously compares current images with reference images and uses the detected differences to automatically adjust and update the region of interest. This feedback loop enables the system to automatically track moving objects by constantly adapting to their new positions based on image difference analysis.
Solution Approach 2:
The system pre-establishes a region of interest based on initial image analysis and reference comparison. This preliminary action allows the system to focus computational resources on tracking specific areas where objects are likely to move, enabling automatic tracking without requiring full-image analysis at each step.
2Loss of information
If video images are displayed on a monitor for monitoring, then the ability to view changes is improved, but the objectivity and ease of detecting changes deteriorates due to subjective human perception
Solution Approach 1:
The system uses color-coded overlays to indicate different types of changes: green for added objects, red for removed objects, and yellow for moved objects. This visual encoding transforms subjective image comparison into an objective, easily interpretable format that eliminates human perception biases while maintaining high change detection accuracy.
Solution Approach 2:
The system segments the image into different change types (added, removed, moved objects) and displays them separately with distinct visual indicators. This segmentation allows for precise, objective detection and classification of changes without relying on human subjective assessment of the entire scene.
3Manufacturing precision
If manual tracking of moving objects is performed by users, then the ability to capture specific images is improved, but the time and effort required deteriorates
Solution Approach 1:
The system performs automatic object tracking and image capture without requiring manual user intervention. It autonomously identifies moving objects, tracks their movement, and captures images at appropriate moments, eliminating the time and effort previously required for manual tracking while maintaining high image quality through automated focus and framing.
Solution Approach 2:
The system dynamically adjusts the region of interest and tracking parameters based on real-time image analysis and object movement detection. This dynamic adaptation allows the system to automatically optimize image quality for moving objects without requiring manual adjustment, reducing tracking time while maintaining precision.
Data Source
AI summary
A monitoring support apparatus which supports a monitoring system using a comparison method for real time and archived film and/or photographs. It relates to image capturing devices and, particularly, to an image capturing device which can automatically compare photographs and/or film and compare the differences in a selected time or an archive to a present situation. This relates to systems for video viewing/monitoring films or photographs and determining what changes have occurred. The process comprises: a general Flicker Process: Step 1: Establish Photograph/Film baseline; Step 2: Select comparison Photograph/Film Future or Past; Step 3: Time Lapse between the baseline and a comparison frame; Step 4: Contrast Base and Comparison Selected Computer App/Streaming/etc.; Step 5: Flash/Color/Highlight/“Box-In”/Ghost etc. differences; Step 6: Record/Save Contrasted Comparison; and Step 7: Take Action: Report, Respond, Call Authorities, or other.