Video Editing Device Continuity Error Detection
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Solution Overview
Problem
Conventional video editing techniques require manual analysis to identify continuity errors in depiction of objects across multiple shots, leading to inaccuracy and reduced quality of video content.
Innovation Solution
A video editing device that automatically checks continuity of objects between filmed shots and provides notifications to editors about deviations, using 3D image generation and comparison of visual attributes such as shape, size, color, and texture, and factual errors based on timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to identify continuity errors, then the editor can detect deviations in object depiction, but the accuracy and reliability of error identification is reduced
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated computer-based vision system. The system uses image processing algorithms and machine learning models to automatically detect and analyze continuity errors in video frames, substituting human editorial review with computational analysis that provides consistent and accurate results without manual intervention.
Solution Approach 2:
The system enables the video content itself to be analyzed and evaluated by the automated detection system. The computer vision algorithms process the video frames, identify objects, track their attributes across frames, and generate continuity error reports autonomously, allowing the content to serve its own quality assessment without requiring external manual review.
2Productivity
If manual analysis is performed to check continuity across multiple shots, then continuity errors can be identified, but the productivity and efficiency of the editing process is reduced
Solution Approach 1:
The system performs preliminary automated analysis of video content during the editing process, proactively identifying continuity errors before finalization. By continuously monitoring and analyzing video frames in real-time or near-real-time, the system detects potential issues early, allowing editors to correct them efficiently without time-consuming manual review cycles.
Solution Approach 2:
The patent replaces the time-consuming manual analysis process with automated computer-based detection. The system processes multiple video frames simultaneously using parallel computing and optimized algorithms, reducing the time required for continuity checking from hours or days of manual review to minutes or seconds of automated processing.
3Measurement precision
If automated detection is implemented, then the accuracy of continuity error identification is improved, but the device complexity increases
Solution Approach 1:
The patent divides the complex continuity detection task into separate modular functional components: object detection module, attribute analysis module, frame comparison module, and error reporting module. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while achieving high detection accuracy through specialized processing in each segment.
Solution Approach 2:
The system introduces an intermediary layer of automated analysis between the raw video content and the editor. This intermediary computer vision system processes the video frames, extracts relevant information about objects and their attributes, and presents processed continuity assessment results to the editor, simplifying the interface while maintaining high detection accuracy through intermediate computational processing.
Data Source
AI summary
A video editing device includes a memory that stores first video content and includes circuitry that extracts a first image frame of a first filmed shot of the first video content and a second image frame of a second filmed shot of the first video content. The first image frame is a last frame of the first filmed shot and the second image frame is a first frame of the second filmed shot. The circuitry detects a first set of objects from the extracted first image frame and a second set of objects from the extracted second image frame. The circuitry further generates and outputs notification information associated with a first object based on a deviation of at least a first visual attribute of the first object in the first image frame from at least a second visual attribute of the first object in the second image frame.