Camera Obstruction Detection via Edge Density Analysis
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Solution Overview
Problem
Automated cameras, such as red light and traffic cameras, face challenges in maintaining an unobstructed field of view due to obstructions like foliage, ice, or debris, which can lead to inaccurate image capture and detection of objects of interest, relying on labor-intensive human monitoring or reference image subtraction methods that are prone to errors and complexity.
Innovation Solution
A method that analyzes images using edge detection to identify regions lacking edge content and compares their size to a threshold, determining obstructions without relying on a reference image, employing edge detection, local averaging, and connected component analysis to generate an edge density metric for obstruction detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image subtraction from a reference image is used to detect obstructions, then obstruction detection capability is provided, but false indications occur in dynamically changing scenes and the process becomes time intensive
Solution Approach 1:
The patent extracts only the essential characteristic needed for obstruction detection - the presence of large out-of-focus regions - rather than performing full image subtraction. By focusing solely on detecting blurred areas through edge density analysis, the system eliminates the time-consuming reference image comparison while maintaining reliable obstruction detection.
Solution Approach 2:
Instead of using a reference image copy for subtraction, the patent creates a simplified representation by analyzing edge density patterns directly in the current image. This copying approach uses only the necessary visual information (edge distribution) rather than the entire image data, reducing processing time while preserving detection accuracy.
2Reliability
If reference image subtraction is used to detect obstructions, then obstruction detection is enabled, but device complexity and processor demands increase
Solution Approach 1:
The patent extracts only the critical feature for obstruction detection - regions with abnormally low edge density - from the complex task of full image subtraction. By isolating and analyzing only the edge density distribution, the system dramatically reduces processor demands and algorithmic complexity while maintaining the ability to reliably detect obstructions.
Solution Approach 2:
The patent changes the detection parameter from pixel-by-pixel image subtraction to edge density analysis. This parameter transformation converts a computationally intensive operation into a simpler statistical analysis of edge distributions, reducing processor complexity while preserving obstruction detection reliability.
3Reliability
If manual inspection by operators is used to monitor camera obstructions, then detection capability is provided, but labor intensity and human error increase
Solution Approach 1:
The patent enables the camera system to perform self-diagnosis by automatically analyzing its own captured images for obstruction indicators. The system independently detects obstructions through edge density analysis without requiring external human operators, eliminating labor intensity while maintaining reliable detection through automated processing.
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated computational system. Instead of operators manually reviewing images, the system uses algorithmic edge density analysis to automatically detect obstructions, eliminating human error and labor intensity while preserving detection reliability.
4Productivity
If automated image analysis is used to detect obstructions, then labor intensity is reduced, but computational complexity and processing demands increase
Solution Approach 1:
The patent extracts only the essential computational element needed for obstruction detection - edge density measurement - from the broader task of automated image analysis. By focusing solely on calculating and comparing edge density statistics rather than performing comprehensive image processing, the system achieves high monitoring efficiency with reduced computational complexity.
Solution Approach 2:
The patent transforms the computational task from complex automated image analysis to a simpler parameter-based approach using edge density metrics. This parameter change enables the system to maintain high productivity through automated monitoring while significantly reducing computational complexity by analyzing only the distribution of edge features rather than entire images.
Data Source
AI summary
A method (100) is provided for detecting an obstruction within a field of view of a camera (12) from an image (200) captured by the camera (12). The method (100) includes: analyzing the image (200) by applying edge detection (104) to the image (200), identifying (108) regions of the image (200) lacking edge content and comparing (112) a size of the identified regions to a threshold; and determining if there is an obstruction based upon a result of said comparison.