研究目的
To develop a video stabilization algorithm that clusters features to improve motion estimation and smoothing for better video quality.
研究成果
The proposed method effectively detects and removes unwanted feature points from moving objects, improving the accuracy of global motion estimation and video stabilization performance.
研究不足
In cases where the speed and direction of moving objects are similar to the camera motion, some outliers may remain in the chosen features, though performance is not significantly reduced.
1：Experimental Design and Method Selection:
The algorithm uses KLT for feature tracking, directional statistics and K-means clustering to separate background and foreground features, and an Alpha-trimmed filter for motion smoothing.
2：Sample Selection and Data Sources:
Numerous video sequences were tested, but specific sources are not detailed.
3：List of Experimental Equipment and Materials:
Not specified in the paper.
4：Experimental Procedures and Operational Workflow:
Features are tracked between consecutive frames, clustered based on direction and magnitude, and motion is smoothed using the filter before warping to stabilized images.
5：Data Analysis Methods:
Performance is evaluated using Inter-frame Transformation Fidelity (ITF) to measure video smoothness.
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