Camera Image Angle Variation Detection Using Fixed Point Stability
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Solution Overview
Problem
Existing image angle detection systems for cameras, especially those with pan/tilt/zoom functions and planetary exploration spacecrafts, face challenges in accurately detecting and correcting image angle variations due to environmental influences such as wind and foreground movements, which are not adequately addressed by current correction methods.
Innovation Solution
An image angle variation detection device and method that utilizes fixed point information, including the position and features of stable points, to detect changes in the image angle over time, determining variations when stable changes are detected for a certain period, and adjusts the camera accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a camera with pan/tilt/zoom function stores orientation data for automatic recovery, then the camera can automatically recover its posture after blackout, but the original image angle cannot be obtained when the camera platform is moved
Solution Approach 1:
The system uses the camera's own imaging function to detect image angle variations by analyzing videos it captures. The camera serves itself by using its imaging capability to monitor and detect its own orientation changes, eliminating the need for external reference systems while maintaining high measurement precision.
2Reliability
If traditional correction methods are used for cameras affected by wind and foreground movement, then correction can be applied, but accurate detection of image angle variation in various shooting environments cannot be achieved
Solution Approach 1:
The system detects image angle variations by analyzing changes in parameter values (position and features of fixed points) extracted from video frames. By continuously monitoring these parameters and detecting stable changes over time, the system can accurately detect image angle variations across different shooting environments including those with wind and foreground movement.
3Measurement precision
If manual confirmation and calibration of camera image angle is performed after earthquake, then accurate correction can be achieved, but it takes considerable time
Solution Approach 1:
The system continuously captures videos, extracts fixed point information, and compares it with reference data to detect image angle variations in real-time. This automated feedback mechanism provides accurate calibration information immediately after disturbances such as earthquakes, eliminating the need for manual confirmation and significantly reducing calibration time while maintaining high accuracy.
4Measurement precision
If fixed point information including position and features is used to detect image angle variation, then accurate detection can be achieved, but the system must determine when changes are stable over a certain period
Solution Approach 1:
The system dynamically determines whether image angle variation has occurred by checking if changes in fixed point information remain stable over a predetermined period. This dynamic threshold approach allows the system to distinguish between temporary fluctuations and actual image angle variations, maintaining high detection accuracy while using a relatively simple stability judgment mechanism.
Data Source
AI summary
An image angle variation detection means 81 detects an image angle variation of a imaging device from videos shot by the imaging device based on fixed point information including the position of a fixed point specified by an image angle of the imaging device and the features indicating the characteristics of the fixed point, and a situation determination means 82 determines that the image angle of the imaging device is varied when a change in the image angle variation is stable for a certain period of time.