Automated Video Continuity Error Detection via Visual Inconsistency Analysis
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Solution Overview
Problem
Conventional methods for identifying visual inconsistencies in videos are inefficient and prone to missing errors, requiring manual review that is time-consuming and costly, leading to low-quality videos with distracting inconsistencies.
Innovation Solution
An automated system analyzes video frames to identify visual inconsistencies by comparing main objects and features between frames and based on semantic analysis, generating an error report to highlight continuity errors, thereby improving video quality and reducing production costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of video is used to identify visual inconsistencies, then human reviewers can spot errors, but the huge amount of film material makes it nearly impossible to spot every error and the process is time-consuming
Solution Approach 1:
The patent replaces the mechanical human review process with an automated computer-based system that uses image processing and comparison algorithms to detect visual inconsistencies. The system automatically analyzes video frames, identifies objects, and compares them across different shots and angles, eliminating the need for manual frame-by-frame review while maintaining high detection accuracy.
Solution Approach 2:
The system creates digital copies of video frames and uses these copies for automated comparison analysis. By working with frame copies rather than the original video stream, the system can perform multiple comparisons and analyses simultaneously, dramatically increasing processing speed while maintaining detection precision.
2Reliability
If conventional manual methods are used for video continuity analysis, then some errors can be identified, but the process is costly and cannot easily identify all visual inconsistencies
Solution Approach 1:
The system performs preliminary automated analysis of video continuity errors before final production completion. By detecting visual inconsistencies early in the editing process through automated frame comparison and object tracking, the system allows for corrections to be made before the video is finalized, reducing both time and cost while improving reliability.
Solution Approach 2:
The system provides automated feedback by generating reports that highlight detected continuity errors with specific location information. This feedback mechanism allows editors to quickly review and correct identified issues, creating a continuous improvement loop that enhances overall video quality while reducing the time needed for manual checking.
3Productivity
If automated systems are implemented for video analysis, then productivity and speed improve, but the complexity of the system increases
Solution Approach 1:
The automated analysis system is divided into distinct functional modules: frame extraction, object detection, object tracking, comparison analysis, and error reporting. Each module handles a specific aspect of the analysis process, making the overall complex system more manageable and easier to implement while maintaining high productivity through specialized processing for each task.
Data Source
AI summary
Embodiments of the present invention are directed towards determining visual inconsistencies in frames of a video to identify continuity errors in the video. A visual inconsistency can be based on an appearance or a disappearance of an object between frames of the video. Visual inconsistencies in the video can also be identified based on a semantic analysis of an object corresponding to a frame of the video. Identified continuity errors can be used to generate an error report.