Traffic Camera Misalignment Detection via Scene Analysis
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Solution Overview
Problem
Conventional traffic camera diagnostics struggle to accurately detect subtle deterioration, such as camera misalignment, due to labor-intensive test pattern analysis and scene-dependent image analysis, which can be costly and yield sub-optimal results, especially for gradual faults.
Innovation Solution
A multi-level diagnostic approach that combines scene analysis to predict potential misalignment and test pattern analysis for confirmation, using thresholding to determine the severity of camera faults and automatically recalibrate or notify maintenance when necessary, thereby optimizing resource use and diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If test pattern analysis is used to detect camera deterioration, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The diagnostic process is segmented into multiple phases: scene analysis phase for initial assessment, test pattern analysis phase for confirmation, and diagnostic response phase for calibration or notification. This segmentation allows the system to use simpler scene analysis for routine monitoring while reserving complex test pattern analysis for confirmed issues, thereby reducing overall system complexity while maintaining precision.
Solution Approach 2:
The system performs preliminary scene analysis on all captured images to identify potential camera deterioration before initiating costly test pattern analysis. This preliminary action filters out false positives and reduces the frequency of complex test pattern analyses, making the overall diagnostic system more cost-effective and less complex while maintaining high detection accuracy.
2Ease of operation
If scene analysis is used for camera diagnostics, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system uses feedback from scene analysis to trigger more precise test pattern analysis when necessary. Scene analysis provides continuous monitoring with simple operations, while test pattern analysis provides precise measurements when scene analysis indicates potential issues. This feedback loop combines the ease of operation of scene analysis with the precision of test pattern analysis.
Solution Approach 2:
The patent merges scene analysis and test pattern analysis into a unified diagnostic system that leverages the strengths of both approaches. Scene analysis provides continuous, simple monitoring while test pattern analysis provides precise measurements when needed, creating a hybrid system that achieves both ease of operation and measurement precision.
3Measurement precision
If test pattern analysis is performed frequently, then measurement precision is improved, but loss of time and resources increase
Solution Approach 1:
The system performs preliminary scene analysis on all images to identify potential camera deterioration before initiating costly and time-consuming test pattern analysis. This preliminary action filters out false positives and reduces the frequency of complex test pattern analyses, thereby reducing time and resource consumption while maintaining high detection accuracy.
Solution Approach 2:
The system applies partial test pattern analysis only when scene analysis indicates potential camera issues, rather than performing complete test pattern analysis continuously. This partial action approach reduces time and resource consumption while maintaining sufficient measurement precision for detecting actual camera faults.
4Measurement precision
If on-site camera inspections are conducted, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent replaces mechanical on-site inspections with automated image-based diagnostic systems. The system uses scene analysis and test pattern analysis of captured images to detect camera misalignment and deterioration, eliminating the need for physical technician presence at camera locations. This substitution maintains measurement precision while eliminating traffic disruption and safety risks associated with on-site inspections.
Data Source
AI summary
A method for determining a response to misalignment of a camera monitoring a desired area includes acquiring temporal related frames from the camera including a reference frame. A pixel location is determined of a reference object from the frames. Using the pixel location of the reference object, a displacement of the camera between a current frame and the reference frame is determined. For the displacement exceeding a first threshold, a new displacement of the camera is measured by introducing at least one additional object to a camera field of view and comparing the new displacement to a second threshold. For the new displacement not exceeding the second threshold, the camera is recalibrated using a determined pixel location and a physical location of the at least one additional object. For the new displacement exceeding the second threshold, notification is provided of a misalignment to an associated user device.