Video Segmentation via Affine Transformation Feedback
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Solution Overview
Problem
In semantic segmentation of videos, the prediction deviation in the first frame leads to positioning offsets in subsequent frames, resulting in low accuracy of target object segmentation.
Innovation Solution
An image segmentation method that involves obtaining historical affine transformation information from previous frames, performing affine transformation, feature extraction, and semantic segmentation to obtain a candidate region image, with revised affine transformation information being used for subsequent frames to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If key point prediction is performed on each frame to obtain transformation parameters, then the segmentation process can be completed, but prediction deviation accumulates across frames leading to positioning offsets and reduced segmentation accuracy
Solution Approach 1:
The patent performs key point prediction and obtains transformation parameters only for the first frame in advance, then uses these pre-obtained parameters for affine transformation of subsequent frames. This preliminary action avoids repeated key point prediction for each frame, maintaining processing speed while preventing prediction deviation accumulation that would otherwise reduce segmentation accuracy.
Solution Approach 2:
The patent uses the segmentation result and feature map of the first frame to revise the transformation parameters, creating a feedback mechanism. The revised parameters from the first frame are then applied to subsequent frames, providing corrective feedback that prevents error accumulation and maintains high segmentation accuracy across the entire video sequence.
2Stability of the object's composition
If transformation parameters from the first frame are used for subsequent frames, then positioning consistency is improved, but the system becomes more sensitive to initial prediction accuracy
Solution Approach 1:
The patent incorporates a feedback mechanism where the segmentation result and feature map of the first frame are used to revise the transformation parameters. This revision process corrects any initial prediction errors, reducing the system's sensitivity to initial prediction accuracy while maintaining positioning consistency across subsequent frames.
Solution Approach 2:
The patent dynamically adjusts transformation parameters based on the revision process. Instead of using fixed initial parameters throughout, the system modifies the parameters using feedback from the first frame's segmentation results, thereby adapting to actual image content and reducing dependence on initial prediction accuracy.
Data Source
AI summary
This application relates to an image segmentation method and apparatus. The method includes obtaining a current frame and historical affine transformation information transmitted by a previous video frame in a video frame sequence; performing affine transformation on the current frame according to the historical affine transformation information to obtain a candidate region image corresponding to the current frame; performing feature extraction on the candidate region image to obtain a feature map corresponding to the candidate region image; performing semantic segmentation based on the feature map to obtain a segmentation result corresponding to a target in the current frame; and revising the historical affine transformation information according to the feature map to obtain updated affine transformation information, and using the updated affine transformation information as historical affine transformation information corresponding to a subsequent video frame in the video frame sequence.