Video Object Segmentation via Inter-Frame Transfer and Lost Object Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video object segmentation methods face challenges in maintaining accurate object segmentation across frames due to changes in object pose, covering, and confusion or loss of objects when multiple objects overlap and separate, leading to transfer failures and reduced accuracy.
Innovation Solution
A method and apparatus for video object segmentation that involves inter-frame transfer of object segmentation results from a reference frame to other frames, determining frames with lost objects, updating segmentation results, and transferring these updates to correct segmentation errors, using techniques such as probability maps, optical flow maps, and neural networks to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inter-frame transfer of object segmentation results is performed from reference frame to other frames, then segmentation efficiency is improved, but segmentation accuracy deteriorates due to object pose changes and overlap confusion
Solution Approach 1:
The patent performs preliminary detection of lost objects in target frames before transferring segmentation results. By identifying frames with lost objects ahead of time and segmenting them separately, the system prepares corrected segmentation data that can then be transferred to subsequent frames, preventing accuracy degradation while maintaining efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where segmentation results are transferred from reference frames to target frames, then the system detects lost objects in the target frames, segments them separately to update the segmentation results, and transfers these updated results back. This closed-loop feedback ensures accuracy is maintained while preserving the efficiency benefits of inter-frame transfer.
2Loss of time
If object segmentation results are transferred across multiple frames, then processing time is reduced, but reliability deteriorates due to transfer failures from large pose changes
Solution Approach 1:
The patent performs preliminary detection of lost objects in target frames before final transfer. By identifying potential transfer failure cases ahead of time and handling them separately through re-segmentation, the system ensures reliable transfer while maintaining the time efficiency of batch processing.
Solution Approach 2:
The patent cushions against potential transfer failures by detecting lost objects in advance and preparing corrected segmentation results before transfer. This preemptive measure ensures that even when large pose changes occur, the segmentation accuracy is preserved and transfer reliability is maintained.
3Measurement precision
If frames with lost objects are identified and re-segmented, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial action by only re-segmenting frames that contain lost objects, rather than re-segmenting all frames. This selective approach maintains high accuracy where needed while avoiding unnecessary computational complexity in frames where transfer succeeded.
Solution Approach 2:
The patent applies local quality by using different processing strategies for different frames: frames with lost objects undergo re-segmentation while frames without lost objects use direct transfer. This localized approach optimizes accuracy for problematic frames while maintaining efficiency for normal frames, balancing accuracy and complexity.
Data Source
AI summary
A method and an apparatus for segmenting a video object, an electronic device, a storage medium, and a program include: performing, among at least some frames of a video, inter-frame transfer of an object segmentation result of a reference frame in sequence from the reference frame, to obtain an object segmentation result of at least one other frame among the at least some frames; determining other frames having lost objects with respect to the object segmentation result of the reference frame among the at least some frames; using the determined other frames as target frames to segment the lost objects, so as to update the object segmentation results of the target frames; and transferring the updated object segmentation results of the target frames to the at least one other frame in the video in sequence. The accuracy of video object segmentation results can therefore be improved.