Image Sequence Motion Recognition via Steady Region Masking
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Solution Overview
Problem
Existing methods for processing image sequences struggle with efficient motion recognition, especially when multiple objects move in different directions, leading to difficulties in feature pair association and high computation complexity, resulting in poor compression gains in image sequences with rotating map regions and static elements like status bars.
Innovation Solution
A method that identifies steady regions in an image sequence, masks them to improve feature pair association, and uses a single motion model to determine model parameters, integrating motion recognition into video coding with minimal additional computation complexity by leveraging skip mode information and RANSAC algorithm for efficient motion compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature detection and association is performed using RANSAC algorithm for motion recognition, then motion parameters can be determined, but computation complexity becomes very high when multiple objects move in different directions
Solution Approach 1:
The patent divides the image sequence into multiple steady regions, each with its own motion model. Instead of applying a single complex RANSAC algorithm to the entire image, the computation is segmented into multiple simpler RANSAC operations on smaller regions, reducing overall computational complexity while maintaining motion recognition accuracy.
Solution Approach 2:
The patent applies RANSAC algorithm only to regions where motion is actually present, rather than processing the entire image. By identifying steady regions and applying motion recognition only where necessary, the computation complexity is reduced while maintaining adequate motion recognition accuracy.
2Measurement precision
If multiple motion models are used to handle different moving objects, then motion recognition accuracy improves, but device complexity and computation complexity increase significantly
Solution Approach 1:
The patent segments the image into multiple steady regions, each handled by a simple motion model. This avoids the need for a single complex multi-model system, as each region independently uses a straightforward motion model, reducing overall system complexity while maintaining accurate motion recognition for each region.
3Loss of information
If feature pairs are associated across the entire image, then all motion information can be captured, but difficulty of detecting and measuring increases when static elements are present
Solution Approach 1:
The patent extracts and identifies steady regions from the image sequence, then excludes these regions from feature pair association. This removes the harmful effect of static elements that complicate feature matching, while motion information from non-steady regions is still fully captured through the region-based approach.
Data Source
AI summary
A method identifies motion in an image sequence, which method involves an image processing device ascertaining a change of position of image features in a current image in respect of a reference image. In the method there is a paired association of corresponding image features, one of which can be found in the current image and one of which can be found in the reference image. Model parameters a determined, which describe the change of position for a motion model on the basis of the image features which are associated with one another. A static region is ascertained for the current image, in which static region the change of position of the image contents contained therein is smaller than a predetermined amount, and the model parameters are determined by using only image features which can be found outside the at least one static region.