Fixed-Field Video Summarization Using Object Classifiers
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Solution Overview
Problem
The increasing volume of digital media content makes it difficult to manually process and identify relevant information, and text-based search engines are limited in identifying subject matter within videos, necessitating a solution to automate the search and generation of summary video content.
Innovation Solution
A system that applies machine-learned classifiers to frames of videos with a fixed field of view to identify instances of specific object types, generating summary videos that include the background and identified instances for simultaneous playback, and responds to user queries using live video streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and curation of video content is performed, then accuracy of summary generation is improved, but productivity and processing speed deteriorate due to the overwhelming volume of digital media
Solution Approach 1:
The patent introduces machine-learned classifiers as an intermediary between the video content and the summary generation process. These classifiers automatically analyze video frames, identify objects, and detect actions, serving as a mediator that bridges manual curation accuracy with automated processing speed. The system uses multiple classifiers (object classifier, action classifier) that work together to provide comprehensive analysis without requiring manual review of every video frame.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine-learning-based system. Instead of human curators manually watching and analyzing videos, the system uses trained classifiers that process video frames automatically. This substitution maintains high accuracy through multiple classification layers while dramatically increasing processing productivity.
2Ease of operation
If text-based search engines are used to identify subject matter in videos, then ease of operation is improved, but measurement precision deteriorates because they cannot effectively identify visual content within videos
Solution Approach 1:
The patent replaces text-based search mechanisms with machine-learned visual classifiers that directly analyze video frames. The object classifier and action classifier process visual content to identify subjects matter accurately, substituting the inadequate text-based approach with a vision-based system that can actually understand what is happening in the video.
Solution Approach 2:
The patent changes the search parameter from text metadata to visual features extracted by machine-learned classifiers. Instead of searching based on video titles or descriptions, the system searches based on actual visual content identified by the object and action classifiers, fundamentally changing how subject matter is identified and improving precision.
3Measurement precision
If multiple machine-learned classifiers are applied to video frames to identify different object types, then measurement precision of object identification is improved, but device complexity increases
Solution Approach 1:
The patent segments the video analysis task into multiple specialized classifiers: an object classifier for identifying what objects are present, an action classifier for detecting what actions are occurring, and potentially other specialized classifiers. Each classifier focuses on a specific aspect of video content, improving overall precision while managing complexity through functional segmentation.
Solution Approach 2:
The patent creates a universal video analysis system where multiple classifiers work together within a single integrated platform. The system processes video frames through multiple classification layers that collectively provide comprehensive analysis, making the complex system reusable across different video content and search queries.
4Reliability
If manual identification and curation of relevant video portions is performed, then reliability of summary content is improved, but loss of time increases due to the extensive manual effort required
Solution Approach 1:
The patent performs preliminary analysis of video content by applying machine-learned classifiers to identify objects, actions, and relevant portions before summary generation. This preliminary classification work automates what would otherwise require manual identification, maintaining reliability through accurate classification while dramatically reducing the time required for summary creation.
Solution Approach 2:
The system enables automated self-service summary generation where the video content is analyzed and summarized without requiring manual human intervention. The machine-learned classifiers automatically identify relevant portions and generate summaries, making the system self-sufficient while maintaining content reliability through multiple classification layers.
Data Source
AI summary
A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.