Auto-Montage Creation Using User Markers
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Solution Overview
Problem
Existing systems for creating video montages from user-generated content require extensive processing and human curation, making it difficult to automatically select and assemble relevant portions of videos from multiple users without significant computational resources.
Innovation Solution
A system that creates a 'bubble' for each theme or event, allowing users to submit media with markers that indicate significant portions, which are then weighted and combined to select the most relevant content for a montage, reducing the need for extensive analysis and human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated video montage creation is implemented using existing systems, then the montage can be created automatically, but extensive processing and computational resources are required
Solution Approach 1:
Users apply markers to videos during upload as a preliminary action, indicating significant portions before the automated montage creation process. This pre-marking eliminates the need for extensive AI-powered video analysis during montage generation, significantly reducing computational resources while maintaining full automation of the montage assembly process
Solution Approach 2:
The system extracts only the marker information from user-submitted videos, rather than processing the entire video content. By taking out only the essential metadata (markers) and using that to guide montage creation, the system achieves automation with minimal computational overhead
2Measurement precision
If extensive processing is used to analyze video content, then relevant portions can be identified accurately, but the processing time and computational demand increase significantly
Solution Approach 1:
Users perform the identification function themselves by applying markers to indicate significant portions of their videos. This self-service approach replaces the need for time-consuming automated video analysis, achieving both high identification accuracy (since users know their content best) and minimal processing time
3Reliability
If manual curation is used to select video portions, then high-quality montages can be created, but human intervention and time are required
Solution Approach 1:
The system merges user-applied markers with automated montage assembly algorithms. Users provide the quality judgment through markers, while the system automatically handles the tedious assembly process, combining the reliability of human curation with the ease of automated operation
Data Source
AI summary
In one embodiment, a montage of media (videos, images, audio, etc.) is created automatically from media submitted by different users and related to a common theme or event. A bubble is created for each theme or event. The users submit the media over the Internet or another network with an indication of a particular bubble the media pertains to. The media in the bubble is examined for objects, audio, engagement metrics, faces, and various other accompanying markers, which are then used to identify media or fragments of media for inclusion in an automatic montage, with music and transition effects.