Automated Asset Tagging for Audio Visual Content Editing
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Solution Overview
Problem
The complexity and cost of editing audio-visual content have increased due to the abundance of digital media formats, making it difficult for directors and editors to recall specific details of assets used in each scene, leading to increased complexity in content acquisition, storage, and display.
Innovation Solution
A system that automates the identification and display of audio-visual content by correlating AV files with asset tags using methods like RFID, barcodes, facial recognition, and voice recognition, allowing users to search and filter content based on assets such as actors, props, and locations, facilitating easier editing and collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple takes and scenes are captured to increase content availability, then the quantity of audio-visual content increases, but the complexity and time required for editing increases
Solution Approach 1:
The system performs preliminary actions by automatically generating asset tags and metadata during or immediately after the filming process, before the editing stage begins. This pre-tagging of assets (actors, props, locations, costumes) with identifying information creates an organized structure that editors can later query and filter, eliminating the need to manually review and catalog numerous takes and scenes during the editing process.
2Quantity of substance
If multiple takes and scenes are captured to increase content availability, then the quantity of audio-visual content increases, but the time required for editing increases
Solution Approach 1:
The system implements feedback mechanisms where asset tags and metadata are automatically generated from scene data and fed back into the editing system. This creates a searchable database that allows editors to quickly retrieve specific takes and scenes based on asset criteria, dramatically reducing the time needed to navigate and select content from large volumes of captured material.
3Measurement precision
If directors and editors need to recall specific details of assets used in each scene, then the measurement precision of asset identification is improved, but the difficulty of detecting and measuring asset details increases
Solution Approach 1:
The system introduces an intermediary layer of automated asset tagging and metadata generation that bridges the gap between the filming process and the editing process. This intermediary system captures asset information (actors, props, locations, costumes) during filming and stores it in a structured format, allowing directors and editors to precisely identify and retrieve specific asset details without having to rely on memory or manually review each scene.
Data Source
AI summary
A system for identifying, tagging, and displaying one or more assets within an audio visual (AV) file includes an asset tagging server, an asset tag acquisition device, and a database, wherein the asset tag acquisition device includes an asset identification engine configured to receive an asset identification data set and generate an asset tag data file that includes an asset tag corresponding to each asset. The asset tagging server is configured to receive, from an AV capture device, an AV file that includes an AV representation of each asset and corresponding timestamp data, and store, in the database, an AV asset tag data file comprising the beginning timestamp, the ending time stamp, and the set of asset tags.