Video Panoptic Segmentation via Temporal Feature Fusion
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Solution Overview
Problem
Conventional panoptic segmentation methods applied to individual video frames result in inconsistencies and are computationally intensive, leading to misidentification of objects and inconsistent class labels across frames.
Innovation Solution
The method identifies a target frame and a reference frame, generates target and reference features, combines them using a spatial-temporal attention module to produce fused features, and creates a feature matrix for accurate panoptic segmentation, incorporating temporal context to ensure consistency across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If panoptic segmentation is applied to each video frame individually, then the segmentation can be computed independently and simply, but the results show inconsistencies between frames and require significant computational resources
Solution Approach 1:
The patent merges the processing of target frame and reference frame by generating features for both frames, fusing these features together, and using the fused features to generate panoptic segmentation for the target frame. This combining approach ensures temporal consistency while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary feature extraction for both the target frame and reference frame before generating the final segmentation. By pre-computing features for the reference frame and fusing them with target frame features, the system ensures consistent results across frames while reducing redundant computations.
2Device complexity
If panoptic segmentation is applied to each video frame individually, then the processing is computationally simpler per frame, but the overall computational intensity increases and accuracy decreases
Solution Approach 1:
The patent combines feature extraction and segmentation processing across multiple frames by fusing reference frame features with target frame features. This merging reduces redundant computations and improves overall processing efficiency while maintaining or enhancing segmentation accuracy.
Solution Approach 2:
The patent maintains continuous useful action by utilizing features from reference frames that are fused with target frame features. This continuous utilization of extracted features across frames improves processing efficiency and accuracy by avoiding redundant feature extraction and leveraging temporal information.
3Reliability
If features from reference frames are fused with target frame features, then temporal consistency and segmentation quality improve, but the computational process becomes more complex
Solution Approach 1:
The patent introduces a feature fusion module as an intermediary that combines features from reference frames and target frames. This intermediary component manages the complexity of temporal feature integration while ensuring consistent and accurate segmentation results across video frames.
Data Source
AI summary
Systems and methods for panoptic video segmentation are described. A method may include identifying a target frame and a reference frame from a video, generating target features for the target frame and reference features for the reference frame, combining the target features and the reference features to produce fused features for the target frame, generating a feature matrix comprising a correspondence between objects from the reference features and objects from the fused features; and generating panoptic segmentation information for the target frame based on the feature matrix.