Media Content Completion Detection Using Frame Changes and ML Tags
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Solution Overview
Problem
Existing media capture devices lack a mechanism to detect the completion of specific types of media content, such as advertisements or public service announcements, leading to user inconvenience when switching channels.
Innovation Solution
A system utilizing a change determination engine and machine learning model to identify changes in media frames, generating tags to determine the completion of specific content types, allowing automatic channel switching or notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually monitors media content to detect completion of specific content types, then the user can switch channels appropriately, but this requires continuous user attention and intervention
Solution Approach 1:
The system performs content completion detection automatically without requiring user intervention. The change determination engine continuously analyzes media frames and the machine learning model identifies content type completion, enabling the system to self-monitor and self-report content status, thereby freeing the user from manual monitoring tasks
Solution Approach 2:
The system provides feedback to the user about content completion status through notifications or automatic channel switching. The change determination engine generates tags indicating content completion, and this information is fed back to control the media player or notify the user, creating a closed-loop system that eliminates the need for continuous user attention
2Ease of operation
If no content completion detection mechanism is implemented, then the system remains simple, but users experience inconvenience when switching channels during specific content types
Solution Approach 1:
The change determination engine and machine learning model act as intermediary components between the media content and the user interface. These intermediaries automatically analyze content frames, determine completion of specific content types, and trigger appropriate actions, thereby providing reliable content completion detection without requiring complex user interaction or system redesign
Solution Approach 2:
The content analysis function is segmented into specialized components: a change determination engine for detecting frame differences and a machine learning model for classifying content types. This segmentation allows each component to focus on a specific task, improving detection reliability while maintaining system modularity and manageable complexity
Data Source
AI summary
Systems and techniques are described herein for processing media content. For example, a process can include obtaining a first media frame and a second media frame. The process can include generating, using a first change detector, a first tag indicating a change above a first change threshold has occurred in the second media frame relative to the first media frame. The process can further include generating, using a machine learning model, a second tag indicating that media content of the second media frame is associated with a particular type of media content. The process can further include determining, based the first tag and the second tag, that the media content of the second media frame is associated with the particular type of media content.