Object Displacement Detection via Difference Image Dot Accumulation
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Solution Overview
Problem
Conventional security monitoring systems face challenges in detecting small object movements, leading to increased false alarms and neglect of significant movements, as they require complex calculations and excessive resource consumption to amplify slight changes.
Innovation Solution
The method involves capturing multiple images of an object, performing difference calculations between successive images to generate difference image dots, and determining object displacement by summing these dots against a threshold, reducing computational load and false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional security monitoring methods are used to detect large movements, then detection capability for obvious movements is maintained, but detection capability for small movements deteriorates
Solution Approach 1:
The patent segments the detection process into multiple stages: capturing multiple images over time, performing difference calculations between successive images to generate difference image dots, and then accumulating these dots. This segmentation allows the system to detect small movements by accumulating evidence over time while filtering out random noise through the threshold mechanism.
Solution Approach 2:
The patent performs preliminary difference calculations between successive images to generate difference image dots before making the final detection decision. This preliminary action of creating difference dots from multiple image pairs allows the system to accumulate movement evidence and distinguish it from random noise, improving small movement detection while maintaining reliability.
2Measurement precision
If Eulerian algorithm is used to amplify transient changes, then slight movements can be visualized, but computational complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the essential information needed for detection by generating difference image dots from difference calculations between successive images. Instead of performing complex full-image processing like the Eulerian algorithm, the system extracts movement information into discrete difference dots and accumulates them, significantly reducing computational complexity while maintaining detection precision.
Solution Approach 2:
The patent uses simple difference image dots as temporary computational objects that are generated, accumulated, and then discarded after threshold comparison. These lightweight difference dots replace the heavy computational burden of the Eulerian algorithm, providing an efficient and resource-friendly approach to detecting slight movements.
3Measurement precision
If multiple images are captured and difference calculations are performed, then detection precision is improved, but processing time increases
Solution Approach 1:
The patent uses periodic action by capturing multiple images at regular time intervals and performing difference calculations between successive images. This periodic approach allows the system to accumulate movement evidence efficiently over time while maintaining real-time processing capability, balancing detection accuracy with processing speed.
Data Source
AI summary
An object displacement detection method includes capturing n images of an object for obtaining n sets of image dots, where the object corresponds to an ith set of image dots in an ith image of the n images; performing (n−1) difference calculations using the n sets of image dots to obtain (n−1) sets of difference image dots, where a jth set of difference image dots of the (n−1) sets of difference image dots is generated by performing a jth difference calculation of the (n−1) difference calculations using a (j+1)th set of image dots and a jth set of image dots of the n sets of the image dots; and determining the object has displaced when a sum of numbers of the (n−1) sets of difference image dots reaches a first threshold.