Video Summarization System with User Feedback Refinement
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Solution Overview
Problem
Current video summarization systems require advanced knowledge and are not user-friendly, making it difficult for non-expert users to generate effective video summaries.
Innovation Solution
A system that uses multiple cameras to capture 360° video data, processing it with remote computing resources to generate video summarizations based on user-defined or inferred parameters, including length, pace, entropy, and style, using computer vision algorithms to identify and rank frames of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If expert users manually create video summarizations using traditional systems, then the quality and precision of video summarization is improved, but the ease of operation deteriorates due to requiring advanced knowledge
Solution Approach 1:
The system enables video summarization to be performed automatically without requiring expert user intervention. The computer executes algorithms that autonomously analyze video content, identify key frames, and generate summaries, allowing non-expert users to obtain professional-quality results through simple operations.
Solution Approach 2:
The patent replaces manual expert analysis with automated computer-based image processing and machine learning algorithms. The system uses computational methods to detect features, rank frames, and construct summaries, substituting human expertise with algorithmic processing while maintaining or improving summary quality.
2Ease of operation
If automated video summarization systems are implemented, then the ease of operation is improved, but the manufacturing precision deteriorates due to lack of expert knowledge
Solution Approach 1:
The system replaces manual expert operations with automated computer vision and machine learning algorithms. These algorithms automatically detect video features, rank frames based on importance, and generate summaries without human intervention, maintaining high precision through sophisticated computational methods.
Solution Approach 2:
The system employs multiple adjustable parameters including frame ranking thresholds, feature detection sensitivity, and summary length controls. These parameters can be optimized and tuned to achieve high summarization accuracy while maintaining automated operation, allowing the system to adapt to different video types and user requirements.
3Adaptability or versatility
If multiple processing parameters are used to tailor video summarizations to user preferences, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The system implements a unified multi-functional platform that handles various summarization tasks through a single integrated architecture. The same core algorithms and processing pipeline support multiple summarization modes, parameter configurations, and output formats, reducing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The system provides dynamic parameter adjustment capabilities where summarization parameters such as length, detail level, and focus areas can be modified based on user preferences and video characteristics. This dynamic adaptability allows the system to optimize performance for different scenarios without requiring separate specialized systems.
Data Source
AI summary
One or more frames of video data may depict content that is determined to likely be of interest to a user. A video segment that includes the one or more frames may be determined. Based at least partly on one or more first summarization parameters associated with the user, a first video summarization may be generated, where the first video summarization includes the first video segment and possibly other video segments associated with the video data. The first video summarization may be provided to the user. Upon receiving data that is representative of user feedback relating to the first video summarization, one or more second summarization parameters may be determined based at least partly on the data. A second video summarization of the video data may be generated based at least partly on the one or more second summarization parameters. The second video summarization may then be provided to the user.