Automated Video Content Analysis System for Consistent Metadata Generation
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Solution Overview
Problem
Existing methods for providing information about movies and video content are labor-intensive and prone to human errors due to manual generation, leading to inconsistencies.
Innovation Solution
Automated analysis using techniques such as automatic speech recognition, natural language understanding, image processing, facial recognition, and speaker identification to generate and annotate video content data, reducing human labor and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate information about video content, then information can be provided to users, but the process is labor-intensive and prone to human errors and inconsistencies
Solution Approach 1:
The patent replaces manual mechanical processes of information gathering and verification with automated computer vision and speech recognition systems. These systems automatically analyze video content to extract information about actors, dialogue, soundtrack, and filming locations, eliminating human labor and associated errors while maintaining high consistency across all generated information.
2Productivity
If manual methods are used to generate information about video content, then information can be provided to users, but the process is very labor intensive
Solution Approach 1:
The system enables video content to analyze and describe itself automatically. By processing the video's own audio and visual data through recognition algorithms, the system generates information about the content without requiring external manual intervention, dramatically improving productivity while reducing labor requirements.
3Measurement precision
If automated analysis techniques are used to generate video content data, then human labor is reduced and data accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex automated analysis system into distinct functional modules: speech recognition for dialogue extraction, natural language understanding for semantic analysis, image processing for visual content analysis, facial recognition for actor identification, and speaker identification for voice attribution. This segmentation manages system complexity by creating specialized, independent components that can be developed and maintained separately while working together to achieve high data accuracy.
Data Source
AI summary
A system and method for using speech recognition, natural language understanding, image processing, and facial recognition to automatically analyze the audio and video data of video content and generate enhanced data relating to the video content and characterize the aspects or events of the video content. The results of the analysis and characterization of the aspects of the video content may be used to annotate and enhance the video content to enhance a user's viewing experience by allowing the user to interact with the video content and presenting the user with information related to the video content.