Fixed-Field Video Summaries Using Instance-Based Object Detection
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Solution Overview
Problem
The increasing volume of digital media content surpasses human review capabilities, and existing text-based search engines struggle to identify relevant subject matter within videos, necessitating a solution for automated video content searching and summary generation.
Innovation Solution
A system that applies machine-learned classifiers to frames of media content with a fixed field of view, generating summary videos that include consistent backgrounds and identified instances of specific object types, 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 deteriorates due to overwhelming volume of digital media
Solution Approach 1:
The patent introduces machine-learned classifiers as intermediary components that automatically analyze video frames and identify objects of interest. These classifiers serve as mediators between the raw video content and the summary generation process, enabling automated content analysis without manual review while maintaining accuracy through trained detection algorithms.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer vision system using machine-learned classifiers. This substitution eliminates the need for human curators to manually watch and analyze videos, instead using automated algorithms to detect, track, and identify objects across video frames, thereby dramatically improving processing productivity.
2Ease of operation
If text-based search engines are used to find video content, then ease of operation is improved, but measurement precision deteriorates because they cannot identify relevant subject matter within videos
Solution Approach 1:
The patent introduces machine-learned object detection classifiers as intermediary analysis tools that bridge the gap between text-based search and actual video content understanding. These classifiers analyze video frames to identify and track specific objects, providing precise subject matter identification that text-based search alone cannot achieve while maintaining user-friendly search interfaces.
3Manufacturing precision
If manual identification and curation of relevant video portions is performed, then manufacturing precision is improved, but loss of time increases due to the extensive manual effort required
Solution Approach 1:
The patent applies preliminary action by using machine-learned classifiers to pre-analyze and identify objects of interest in video frames before summary generation. The system proactively detects, tracks, and labels objects across video sequences in advance, preparing structured data that can be quickly assembled into summaries without requiring manual review of the entire video content.
Solution Approach 2:
The patent replaces the manual identification and curation process with automated computer vision technology. Machine-learned classifiers automatically detect, track, and identify objects throughout the video, eliminating the need for manual frame-by-frame analysis while maintaining high-quality summary content generation.
4Quantity of substance
If comprehensive video analysis is performed to identify all objects, then quantity of information is improved, but device complexity increases due to the need for multiple classifiers and processing stages
Solution Approach 1:
The patent applies universality by designing machine-learned classifiers that can detect and identify multiple types of objects across different video frames using a unified system architecture. The classifiers are configured to handle various object types (vehicles, pedestrians, animals, etc.) through a single multi-functional detection framework, reducing the need for separate specialized systems for each object category.
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.