Video Interference Detection via Edge Intersection and Block Segmentation
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Solution Overview
Problem
Intelligent video monitoring systems face challenges in detecting interference effectively, particularly in large-scale systems where camera positions and directions are fixed, leading to difficulties in distinguishing between normal changes and interference-caused changes in video content.
Innovation Solution
A video image interference detection method that involves acquiring a background image and a current frame image, constructing edge images, intersecting these images to obtain a same boundary image, counting pixel numbers, and determining interference based on a pixel ratio, allowing for differentiation between various types of interference such as occlusion, steering, and out-of-focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frame difference method is used for interference detection, then detection speed and real-time performance are improved, but false alarm rate increases due to sensitivity to momentary changes
Solution Approach 1:
The patent segments the image into multiple blocks and performs interference detection on each block independently. By dividing the image into N×M blocks and calculating interference values for each block separately, the method reduces the impact of momentary changes on the entire image, thereby lowering false alarm rates while maintaining detection speed.
Solution Approach 2:
The patent applies different interference detection thresholds and criteria to different local blocks of the image. Each block can have its own interference determination standards based on local characteristics, allowing the system to be more tolerant of momentary changes in some areas while remaining sensitive to actual interference in others.
2Measurement precision
If background subtraction method is used for interference detection, then detection accuracy is improved, but calculation complexity and operation time increase
Solution Approach 1:
The patent divides the image into multiple blocks and performs background subtraction operations on each block independently rather than on the entire image. This segmentation reduces the computational complexity of each operation while maintaining overall detection accuracy through aggregated block-level results.
Solution Approach 2:
The patent performs background subtraction only on necessary blocks or regions rather than processing the entire image uniformly. By selectively applying the computationally intensive background subtraction method only where needed, the system achieves good detection accuracy while reducing overall calculation complexity and operation time.
3Measurement precision
If background subtraction method is used for interference detection, then detection accuracy is improved, but operation speed decreases due to large amount of calculation
Solution Approach 1:
The patent segments the image into multiple smaller blocks and performs background subtraction on each block independently. This division reduces the computational burden of each operation, allowing parallel processing and improving overall operation speed while maintaining detection accuracy through the aggregation of block-level results.
Solution Approach 2:
The patent applies background subtraction method selectively to specific blocks rather than processing the entire image with the same computational intensity. This partial action approach maintains detection accuracy in critical regions while reducing overall calculation time and improving operation speed.
Data Source
AI summary
A video image interference detection method includes the following steps: acquiring a background image and a current frame image; constructing an edge image of the background image and an edge image of the current frame image respectively; intersecting the edge image of the background image and the edge image of the current frame image to obtain a same boundary image between the edge image of the background image and the edge image of the current frame image; counting a first pixel number in the edge image of the background image and a second pixel number in the same edge image; determining whether the current frame image is interfered according to a ratio of the first pixel number to the second pixel number.