AR Content Management System Using Automatic Still Image Extraction
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Solution Overview
Problem
Existing augmented reality (AR) content management systems require complex processes for users to generate and manage AR content, including still image extraction, marker generation, and association, which hinder widespread adoption by general users.
Innovation Solution
A content management system that simplifies the process by generating still image data from moving image data, allowing users to select and register still images as management markers, issuing access keys, and generating management image data for easy content creation and reproduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually extract still images, generate markers, and associate them with moving images, then AR content can be created with high precision and control, but the process becomes complex and time-consuming
Solution Approach 1:
The system automatically extracts still images from moving images in advance and prepares them as candidate markers before the user needs to create AR content. This preliminary extraction eliminates the need for users to manually extract images at the time of content creation, reducing process complexity while maintaining precision through automated quality selection algorithms.
Solution Approach 2:
The system performs self-service by automatically generating candidate management markers from the moving image data without requiring user intervention. The automated extraction and selection process serves itself, freeing users from manual image extraction tasks while maintaining high precision through built-in quality assessment mechanisms.
2Manufacturing precision
If users manually extract still images and register them as markers, then marker quality and relevance can be controlled, but the time required for content generation increases significantly
Solution Approach 1:
Still images are extracted and prepared as candidate markers in advance, before the user initiates content creation. This preliminary action compresses the time required during actual content generation while maintaining quality through automated selection algorithms that identify relevant frames based on motion detection and scene analysis.
Solution Approach 2:
The system extracts multiple candidate still images (excessive action) from the moving video, providing more options than the user would typically need to manually select. This approach ensures high marker quality by offering multiple relevant candidates while reducing total processing time compared to manual extraction of fewer images.
3Ease of operation
If the system automatically generates still images from moving images, then content generation becomes easier and faster, but user control over marker selection is reduced
Solution Approach 1:
The system segments the marker selection process into two parts: automatic extraction of candidate markers by the system, and selective confirmation or modification by the user. This segmentation provides ease of operation through automation while preserving user control flexibility by allowing users to review and select from presented candidates rather than having completely automated selection.
Solution Approach 2:
The system provides feedback to users by presenting automatically extracted candidate markers for review and selection. This feedback loop maintains user control and adaptability while leveraging automated processing for ease and speed, as users can quickly review pre-processed candidates rather than performing manual extraction from scratch.
4Reliability
If multiple processes are required for AR content creation, then content accuracy and professionalism can be maintained, but accessibility to general users is reduced
Solution Approach 1:
The system performs self-service by automatically executing the complex sequence of processes (image extraction, marker generation, association) without requiring user expertise. This maintains content accuracy through built-in quality control mechanisms while dramatically improving accessibility by eliminating the need for users to understand or manage the underlying complex processes.
Solution Approach 2:
The system consolidates multiple specialized functions (video analysis, image extraction, marker generation, data association) into a single multi-functional platform. This universality maintains professional-grade content accuracy while improving accessibility by providing all these functions through one interface rather than requiring users to coordinate multiple separate tools and processes.
Data Source
AI summary
In a content management system, a still image generation unit generates at least one piece of still image data based on moving image data. A still image selection unit causes a user to select one piece of still image data from among the generated at least one piece of still image data. A management marker registration unit registers the selected still image data or information based on the selected still image data as a management marker in association with the moving image data. An access key issuing unit issues an access key for accessing the moving image data. And a management image generation unit generates management image data including the still image data and the access key.