Transformer Network Video Scene Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for dividing video sequences into semantically different scenes are inefficient, often requiring extensive manual labeling of video frames and prone to over-segmentation, especially when training examples are scarce, leading to inaccurate action detection in surveillance systems.
Innovation Solution
A method utilizing a transformer network to process feature and scene representations, enabling direct class assignment at the scene level, with attention blocks to capture feature and scene interactions, and an auxiliary decoder to improve time axis region localization, reducing the need for frame-level classification and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame-level classification is used to segment video scenes, then classification accuracy can be improved, but the system becomes prone to over-segmentation and requires extensive manual labeling
Solution Approach 1:
The patent segments the video sequence into scenes at a higher level rather than classifying individual frames. By working at the scene level, the system avoids over-segmentation while maintaining classification accuracy, as scenes are defined by semantic coherence rather than frame-by-frame changes
Solution Approach 2:
The patent introduces scene-level representations as intermediaries between frame-level features and final classification. These scene representations aggregate information from multiple frames, enabling the system to distinguish between transient frame variations and meaningful scene changes, thereby reducing over-segmentation
2Reliability
If extensive manual labeling of video frames is performed, then training data quality improves, but the time and resource requirements increase significantly
Solution Approach 1:
The patent extracts scene-level semantic information from video sequences without requiring frame-by-frame labeling. By operating at the scene level, the system can utilize fewer labeled examples while maintaining training data quality, as scene-level labels capture the essential semantic meaning without requiring detailed frame-level annotations
Solution Approach 2:
The patent changes the granularity parameter from frame-level to scene-level classification. This parameter change fundamentally alters the training requirements, allowing the system to achieve reliable training data quality with significantly reduced labeling effort, as scene-level labels are fewer and more representative
3Productivity
If traditional neural networks with limited receptive field are used, then computational efficiency is maintained, but the ability to capture long-range dependencies in video sequences is limited
Solution Approach 1:
The patent transitions from spatial/conventional temporal processing to attention-based processing that operates in a different dimensional space. The attention mechanism allows the network to capture long-range dependencies by weighting relationships between frames based on semantic relevance rather than being constrained by fixed receptive field sizes
Solution Approach 2:
The patent employs a transformer-based architecture that serves multiple functions simultaneously: it extracts features, captures long-range dependencies, and performs scene segmentation. This multi-functional approach eliminates the need for separate components to handle different tasks, maintaining computational efficiency while capturing long-range dependencies
Data Source
AI summary
A method for transforming a frame sequence of video frames into a scene sequence of scenes. In the method: features are extracted from each video frame, and are transformed into a feature representation in a first working space; a feature interaction of each feature representation with the other feature representations is ascertained, characterizing a frame prediction; the class belonging to each already-ascertained scene is transformed into a scene representation in a second working space; a scene interaction of a scene representation with each of all the other scene representations is ascertained; a scene-feature interaction of each scene interaction with each feature interaction is ascertained; and from the scene-feature interactions, at least the class of the next scene in the scene sequence that is most plausible in view of the frame sequence and the already-ascertained scenes is ascertained.