Video Summarization via Group Sparsity Analysis
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Solution Overview
Problem
Existing key frame extraction methods are sub-optimal for consumer videos captured in unconstrained environments, lacking structure and diversity, and require complex steps like camera motion estimation and shot detection, making them inefficient for forming video summaries.
Innovation Solution
A method using group sparsity analysis to select and combine video frames, reducing computational complexity by focusing on temporal grouping and intra-group frame correlation, without the need for camera motion estimation or shot detection, and featuring a data processor-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional key frame extraction methods are used, then key frames can be extracted from structured videos, but the methods become sub-optimal and inefficient for consumer videos captured in unconstrained environments
Solution Approach 1:
The patent changes the fundamental parameters of video representation from traditional shot-based or segment-based models to a sparsity-based model that represents video frames as linear combinations of atomic frames. This parameter transformation enables the system to handle unconstrained consumer videos effectively, as the sparsity model can adapt to diverse and unstructured content without requiring pre-imposed temporal structures.
Solution Approach 2:
The patent replaces complex mechanical processing steps such as camera motion estimation and shot detection with a sparsity-based optimization approach. By formulating key frame extraction as a sparsity optimization problem, the system eliminates the need for these computationally intensive and often unreliable mechanical analysis steps, thereby improving efficiency and adaptability simultaneously.
2Measurement precision
If shot-based or segment-based key frame extraction is used, then key frames can be identified in structured videos, but the computational complexity increases due to camera motion estimation and shot detection steps
Solution Approach 1:
The patent extracts and removes the complex computational steps of camera motion estimation and shot detection from the key frame extraction pipeline. By taking out these unnecessary steps and retaining only the essential sparsity optimization, the system achieves the same key frame identification accuracy with significantly reduced computational complexity.
Solution Approach 2:
The patent substitutes complex mechanical analysis systems with a mathematical optimization approach. Instead of using computationally intensive camera motion estimation and shot detection algorithms, the system uses sparsity optimization to directly identify key frames, thereby reducing device complexity while maintaining measurement precision.
3Stability of the object's composition
If sparsity-based model is applied to video frames, then temporal grouping and intra-group frame correlation are maintained, but the computational complexity is reduced compared to traditional methods
Solution Approach 1:
The patent changes the representation parameters of video frames from traditional temporal sequences to a sparsity-based linear combination of atomic frames. This parameter transformation inherently maintains temporal grouping and intra-group frame correlation while simplifying the computational structure, as the sparsity optimization naturally groups temporally related frames without requiring explicit temporal analysis.
Data Source
AI summary
A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.