Video Segmentation With Clip-Level Tracking for Temporal Consistency
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Solution Overview
Problem
Existing video segmentation techniques struggle with the complexity of video data and high computational cost, failing to effectively capture both within-clip and long-term temporal consistency in video segmentation tasks.
Innovation Solution
A system comprising a within-clip tracking sub-model and a cross-clip tracking sub-model, utilizing axial-trajectory attention and multi-scale deformable attention to enhance local and global temporal consistency, allowing for efficient segmentation of videos into class-labeled tubes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing video segmentation techniques are used, then video segmentation can be performed, but computational cost is high and temporal consistency is poor
Solution Approach 1:
The patent divides video segmentation into two separate tasks: within-clip segmentation and cross-clip tracking. The within-clip segmenter processes individual clips to generate segmentations, while the cross-clip tracker links segments across clip boundaries. This segmentation of the overall task reduces computational complexity by avoiding full video processing in a single pass while improving temporal consistency through specialized handling of temporal relationships.
Solution Approach 2:
The patent introduces segment queries as an intermediary representation that bridges within-clip segmentation and cross-clip tracking. These segment queries encode segment information and are used to match segments across different clips, enabling efficient temporal consistency enforcement without requiring complex direct video-level processing.
2Measurement precision
If full video segmentation is performed, then complete video understanding is achieved, but processing time increases
Solution Approach 1:
The patent segments video processing into independent clip-level operations followed by efficient cross-clip matching. By processing clips independently through the within-clip segmenter and then performing targeted matching using segment queries, the system achieves complete video segmentation accuracy with reduced processing time compared to traditional full-video approaches.
Solution Approach 2:
The patent performs preliminary within-clip segmentation to generate segment queries before cross-clip matching. This preliminary action prepares segment representations in advance, enabling faster and more accurate cross-clip temporal consistency enforcement without requiring reprocessing of entire video sequences.
3Duration of action of stationary object
If traditional tracking methods are used, then object tracking is performed, but long-term temporal consistency is poor
Solution Approach 1:
The patent extends tracking from traditional spatial dimensions to include temporal dimension by processing multiple clips and enforcing cross-clip consistency. The cross-clip tracker operates in the temporal dimension, using segment queries to match segments across time, thereby achieving long-term temporal consistency while maintaining tracking accuracy through the specialized cross-clip matching mechanism.
Data Source
AI summary
The present disclosure describes techniques for implementing video segmentation. A video is divided into a plurality of clips. Each of the plurality of clips comprises several frames. Axial-trajectory attention is applied to each of the plurality of clips by a first sub-model. Clip features corresponding to each of the plurality of clips are generated by the first sub-model. A set of object queries corresponding to each of the plurality of clips is generated based on the clip features by a transformer decoder. Trajectory attention is applied to refine sets of object queries corresponding to the plurality of clips by a second sub-model. Video-level segmentation results are generated based on the refined object queries.