Video Scene Change Detection Using Subtitle Gaps
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Solution Overview
Problem
Traditional video editing techniques for scene change detection are time-consuming and processor-intensive, especially with the transition to digital storage, as they often require manual human intervention and complex algorithms that overwhelm computer systems.
Innovation Solution
A system and method that utilize processing modules to analyze consecutive video frames and subtitle files to identify scene changes by determining the level of difference between frames and correlating this with gaps in conversation, reducing processing burdens by leveraging subtitle information and machine vision analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If complex algorithms are used for automated scene detection, then automation level improves, but processor burden and runtime increase
Solution Approach 1:
The patent segments the video analysis task by extracting only key frames at detected scene changes rather than processing the entire video continuously. The system divides video processing into discrete segments triggered by scene change detection events, reducing overall computational burden while maintaining automation.
Solution Approach 2:
The system performs preliminary actions by pre-defining scene change detection criteria and thresholds before video analysis begins. Subtitle gap detection and frame difference thresholds are established in advance, allowing the system to quickly evaluate video content without complex real-time computations.
2Extent of automation
If complex algorithms are used for automated scene detection, then automation level improves, but runtime increases
Solution Approach 1:
The patent extracts only the essential information needed for scene detection—specifically subtitle text and key frame images—rather than processing entire video files. By taking out only the relevant data elements (subtitle gaps and frame differences), the system achieves automated detection with significantly reduced runtime.
Solution Approach 2:
The system performs partial action by detecting only scene changes rather than analyzing all video content in detail. It uses a threshold-based approach that processes only when scene changes are detected, avoiding excessive computation on stable video segments and reducing overall runtime.
3Measurement precision
If traditional video processing methods are used, then scene detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent introduces subtitle information as an intermediary element that mediates between video content and scene detection. By using subtitle gaps as an intermediate indicator of scene changes, the system achieves accurate detection without directly processing and comparing entire video frames, thereby improving processing speed while maintaining accuracy.
4Device complexity
If manual scene detection is used, then processing burden reduces, but time consumption and labor requirements increase
Solution Approach 1:
The system implements self-service by automatically detecting scene changes using predefined criteria without requiring manual intervention. The automated analysis of subtitle gaps and frame differences enables the system to identify scene changes independently, eliminating the need for dedicated technicians while reducing overall time consumption compared to manual review processes.
Data Source
AI summary
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving at least two consecutive frames of a video file; determining a level of difference between the at least two consecutive frames of the video file; receiving a subtitle file associated with the video file; analyzing the subtitle file to identify a gap in conversation in the video file; and identifying a scene change in the video file when: (1) the level of difference between the at least two consecutive frames of the video file is above a predefined threshold; and (2) the level of difference between the at least two consecutive frames of the video file occurs during the gap in the conversation. Other embodiments are disclosed herein.