Dynamic Video Augmentation via Surface Detection on Mobile Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for inserting third-party video content into original video programming are inefficient, labor-intensive, and computationally demanding, requiring expensive systems and human intervention, while also risking errors in Digital Rights Management compliance.
Innovation Solution
A system and method for dynamically augmenting videos on mobile devices by analyzing source videos to identify surfaces, comparing them to a database, scoring potential matches, and integrating augmentation objects based on constraints, allowing for seamless in-video insertion without ongoing server assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional surface detection and augmentation techniques are used to insert third-party video content into original programming, then the insertion can be performed with automated systems, but the computational resources and data processing requirements become excessively large
Solution Approach 1:
The video processing is divided into discrete surface detection units, where each surface in the video is independently identified and evaluated. This segmentation allows the system to process only relevant portions of the video rather than the entire video stream, reducing overall computational load while maintaining automation.
Solution Approach 2:
Instead of processing every possible surface and frame, the system applies partial action by selectively analyzing only those surfaces that meet specific criteria (such as surface type, location, and suitability for content insertion). This reduces computational resources while maintaining effective automation for appropriate content placement.
2Measurement precision
If manual identification and modification of video scenes by human annotators is used, then accuracy in identifying insertion locations can be ensured, but the process becomes labor intensive and time consuming
Solution Approach 1:
The system performs self-service by automatically detecting surfaces, evaluating their suitability for content insertion, and identifying optimal locations without requiring human annotators. The automated surface detection algorithm independently analyzes video frames, extracts surface information, and produces insertion recommendations, eliminating the time-consuming manual process while maintaining consistent accuracy.
Solution Approach 2:
The manual mechanical process of human annotators visually inspecting and marking video scenes is replaced with an automated computational system that uses image processing algorithms to detect and evaluate surfaces. This substitution maintains precision in location identification while dramatically reducing processing time and labor requirements.
3Manufacturing precision
If extensive data processing and computational analysis are performed to identify and insert third-party content, then the accuracy of content placement can be improved, but the complexity of the system increases
Solution Approach 1:
The complex content insertion task is segmented into distinct modular components: surface detection module, surface evaluation module, content matching module, and insertion rendering module. Each module handles a specific aspect of the process independently, making the overall system more manageable and easier to implement while maintaining high precision through coordinated operation of these segmented functions.
Data Source
AI summary
Disclosed are systems and methods for rendering augmented videos on mobile devices and computing environment with limited computational resources. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video augmentation object experience for viewing users. The disclosed framework operates by leveraging available surfaces in digital content to show augmentation objects in compliance with various pre-established contextual and technical constraints. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the digital content based on the contextual and technical constraints, and the computational resources of the device augmented digital content is rendered on.