Video Feature Detection and Clip Categorization
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Solution Overview
Problem
Media content providers face challenges in efficiently analyzing and representing vast libraries of video content for creating trailers, slideshows, and selecting ideal footage, as manual review and classification are resource-intensive and time-consuming.
Innovation Solution
A system and method for automatically detecting and categorizing representational features within video content, such as shots and frames, using metadata analysis to identify and tag clips and images based on characteristics, allowing for efficient creation of representational materials like trailers and slideshows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and classification methods are used to analyze video content, then accuracy in identifying representational features can be maintained, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computer-based analysis systems. The system uses algorithms to detect representational features, analyze video content, and generate metadata automatically, substituting human labor with computational processes that consume fewer resources while maintaining or improving accuracy.
Solution Approach 2:
The system enables video content to be automatically analyzed and tagged without requiring external manual intervention. The automated detection and classification processes allow the content itself to be processed and organized through self-service mechanisms, reducing the need for human reviewers while efficiently identifying representational features.
2Measurement precision
If manual review and classification methods are used to analyze video content, then thorough analysis can be achieved, but time consumption increases significantly
Solution Approach 1:
The patent replaces time-consuming manual analysis with automated computational systems that can process video content rapidly. The computer-based detection and classification algorithms analyze representational features much faster than human reviewers, maintaining thoroughness while dramatically reducing time consumption.
Solution Approach 2:
The system performs preliminary automated analysis and pre-tagging of video content before final processing or human review if needed. This preliminary action filters and organizes content in advance, reducing the time required for subsequent analysis while ensuring thorough coverage of representational features.
3Productivity
If automated detection methods are implemented, then resource efficiency and speed improve, but system complexity increases
Solution Approach 1:
The patent divides the automated analysis system into modular functional components, each handling specific tasks such as feature detection, classification, and metadata generation. This segmentation allows the complex system to be managed through independent modules, improving productivity while making the overall system complexity more manageable through clear functional separation.
Solution Approach 2:
The system employs multi-functional algorithms and processing mechanisms that can handle various types of video content and representational features through unified approaches. This universality reduces system complexity by avoiding the need for separate specialized systems for different content types, while maintaining high productivity across diverse media.
Data Source
AI summary
A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.