Digest Video Scene Extraction Using Integrated Importance Models
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Solution Overview
Problem
Existing methods for automatically extracting important scenes in video digests using machine learning struggle to include related scenes close to the important scenes, resulting in incomplete or unsatisfactory digest videos.
Innovation Solution
A video processing device and method that utilizes multiple models trained with shifted correct answer data to calculate and integrate importance scores, allowing for the extraction of important scenes and their related scenes by adding importance values across different time frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single machine learning model is used to extract important scenes, then the extraction process is simple and fast, but related scenes close to the important scenes are not appropriately included in the digest video
Solution Approach 1:
The patent divides the scene extraction task into multiple independent models, each responsible for detecting important scenes from different perspectives or temporal contexts. By segmenting the detection function across multiple specialized models, the system achieves both efficiency (each model processes independently) and completeness (collectively covering related scenes)
Solution Approach 2:
The patent combines the detection results from multiple individual models by integrating their importance scores. This merging process allows the system to leverage the strengths of each model while producing a comprehensive digest video that includes both important scenes and related scenes, resolving the contradiction between simplicity and completeness
2Reliability
If multiple models are used to calculate importance, then related scenes are included in the digest video, but the processing complexity and computational cost increase
Solution Approach 1:
The patent manages complexity by standardizing the output parameters of multiple models to a common importance score format. This parameter unification allows different models with potentially different architectures to be integrated through simple arithmetic operations, reducing the complexity of model integration while maintaining the ability to detect related scenes
Data Source
AI summary
In a video processing device, a video acquisition means acquires a material video. An importance calculation means calculates importance in the material video using a plurality of models. An importance integration means integrates the importance calculated using the plurality of models. A generation means extracts important scenes in the material video based on the integrated importance and generates a digest video including the extracted important scenes.