Video Stabilization Using Geometrically Biased Historically Weighted RANSAC
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Solution Overview
Problem
Existing video stabilization techniques fail to effectively remove unwanted camera motion and rolling shutter effects, leading to unstable video footage, especially in hand-held recordings.
Innovation Solution
A method employing homography-based video stabilization and smoothing, which identifies robust image feature points, uses a geometrically biased historically weighted RANSAC algorithm to determine dominant motion, and applies smoothing functions to stabilize video sequences, incorporating historical metrics and geometric components to minimize spatial distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video stabilization techniques are used, then processing speed is maintained, but video stability and distortion reduction are insufficient
Solution Approach 1:
The patent segments the video processing into distinct phases: feature point detection, matching, homography calculation, and stabilization application. This segmentation allows each component to be optimized independently, improving overall video stability while managing computational complexity through modular processing.
Solution Approach 2:
The patent performs preliminary actions by detecting and tracking feature points across frames before stabilization is applied. Historical metrics are pre-calculated for each feature point, and homographies are determined in advance, enabling more accurate distortion reduction during the actual stabilization process.
2Measurement precision
If feature points are densely sampled in high-detail areas, then matching accuracy improves, but spatial uniformity of motion consensus deteriorates
Solution Approach 1:
The patent applies local quality by using non-maximum suppression to reduce feature point density in high-concentration areas while maintaining sufficient points for accurate matching. This creates a more uniform spatial distribution of feature points, ensuring that motion consensus is representative across the entire image rather than being dominated by dense clusters in high-detail regions.
3Stability of the object's composition
If historical metrics are heavily weighted for prior inliers, then motion tracking consistency improves, but adaptability to new motion patterns deteriorates
Solution Approach 1:
The patent implements dynamics by making the weighting of historical metrics adaptive rather than static. The weight given to historical inlier status varies based on the current frame's characteristics and the feature point's tracking history. This dynamic weighting allows the system to maintain consistency for well-tracked points while remaining adaptable to new motion patterns when conditions warrant.
Data Source
AI summary
A method of removing unwanted camera motion from a video sequence is provided. The method matches a group of feature points between each pair of consecutive video frames in the video sequence. The method calculates the motion of each matched feature point between the corresponding pair of consecutive video frames. The method calculates a set of historical metrics for each feature point. The method, for each pair of consecutive video frames, identifies a homography that defines a dominant motion between the pair of consecutive frames. The homography is identified by performing a geometrically biased historically weighted RANSAC on the calculated motion of the feature points. The geometrically biased historically weighted RANSAC gives a weight to the calculated motion of each feature point based on the historical metrics calculated for the feature point. The method removes the unwanted camera motion from the video sequence by using the identified homographies.