Image Stabilization Using Feature Point Homography
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Solution Overview
Problem
Existing image stabilization methods for mobile video cameras struggle to effectively separate the smooth trajectory of the camera's motion from shaking components caused by external factors like wind or hand movement, especially in varying environments, leading to poor stabilization effects due to improper parameter settings.
Innovation Solution
An image stabilization method and device that uses feature point detection to calculate a homography transform matrix, allowing for consistent transform relationships between images, thereby eliminating the need to find a specific motion model or set parameters for smooth trajectory adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parameter adjustment methods are used for smooth trajectory estimation, then stabilization accuracy can be improved in specific conditions, but the system becomes complex and requires environment-specific parameter tuning
Solution Approach 1:
The patent transforms the problem from adjusting stabilization parameters to adjusting playback speed parameters. By changing the parameter space from trajectory smoothness parameters to speed consistency parameters, the system achieves high accuracy without requiring environment-specific tuning. The homography transform matrix and its derivative provide a natural parameterization that adapts to different environments automatically.
Solution Approach 2:
The system uses the video content itself to determine the appropriate stabilization approach. By analyzing feature point movements and computing homography transforms from the video data, the system automatically adapts to different environments without external parameter input. The method serves itself by deriving all necessary information from the input video sequence.
2Reliability
If different motion models are selected for different environments, then stabilization performance can be optimized, but the system requires complex model selection and parameter adjustment
Solution Approach 1:
The patent creates a universal stabilization method that works across different environments (fixed cameras, mobile carriers, vehicles, airplanes) without requiring environment-specific models. The homography transform approach provides a unified framework that handles various motion types and environmental conditions through a single consistent methodology, eliminating the need for model selection.
Solution Approach 2:
Instead of selecting motion models to fit environments, the patent inverts the approach by using environment-agnostic feature point tracking and homography computation. The method derives stabilization parameters from the actual video content rather than imposing pre-defined motion models, allowing automatic adaptation to any environment through content analysis.
3Stability of the object's composition
If trajectory smoothing is applied to remove shaking components, then image stability improves, but the transform relationship between adjacent images becomes inconsistent
Solution Approach 1:
The patent dynamically adjusts the stabilization approach by computing the homography transform matrix and its time derivative for each frame pair. This dynamic computation allows the system to maintain transform consistency while removing shaking, as the parameters are continuously adapted to the current motion state rather than applying fixed smoothing filters.
Solution Approach 2:
The system uses feedback from the homography transform computation to guide the stabilization process. By calculating the transform matrix and its derivative from adjacent frames, the system receives feedback about the actual motion occurring, which is then used to compute stabilization parameters that maintain both stability and transform consistency.
4Manufacturing precision
If feature point detection and homography transform methods are used, then transform consistency between images is achieved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for stabilization from the video content. By focusing on feature point detection and homography transform computation rather than processing all pixels, the method achieves transform consistency with reduced computational load. The approach extracts key geometric relationships without unnecessary processing.
Data Source
AI summary
An image stabilization method and an image stabilization device are provided. In the method, each of images to be processed is detected by a feature point detection method to detect a plurality of feature points. The relationship of the same feature points in adjacent images to be processed is analyzed. According to the relationship of the feature points, a homography transform matrix of adjacent images to be processed is calculated. Based on the known feature points and the homography transform matrix, a stabilization matrix and a plurality of adjustment matrices corresponding to each image to be processed are calculated. Compensation is performed on each image to be processed by the adjustment matrices, so as to produce a plurality of corrected images. A first image of adjacent corrected images multiplied by the same stabilization matrix is transformed to a second image of the adjacent corrected images.