Audiovisual Scene Detection and Text Indexing
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Solution Overview
Problem
Current techniques for scene detection in audiovisual content are labor-intensive and time-consuming, leading to missed opportunities and increased operational and marketing costs due to inadequate tagging.
Innovation Solution
An electronic device system is configured to automatically segment audiovisual content into scenes, extract textual characters, and store them in a searchable database based on timing information, using visual characteristics and optical character recognition to reduce computational burden and enhance searchability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging techniques are used for scene detection in audiovisual content, then tagging accuracy can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables automatic scene detection and tagging by having the audiovisual content itself provide the necessary information through visual analysis and audio transcription, eliminating the need for manual human intervention while maintaining tagging accuracy
Solution Approach 2:
Manual mechanical tagging processes are replaced with automated computational systems that use visual characteristic analysis and audio transcription to detect scenes and generate tags automatically, significantly reducing time consumption
2Adaptability or versatility
If comprehensive tagging is performed on all audiovisual content, then content searchability improves, but operational and marketing costs increase
Solution Approach 1:
The system extracts only the essential and relevant information from audiovisual content through selective scene detection and key frame analysis, rather than processing every frame or detail, thereby improving searchability while reducing operational costs
Solution Approach 2:
The system performs partial processing by focusing on representative frames and key scenes rather than comprehensive analysis of all content, achieving sufficient searchability without the excessive computational cost of complete tagging
3Measurement precision
If scene segmentation is performed using detailed visual analysis, then scene detection accuracy improves, but computational burden increases
Solution Approach 1:
The system segments audiovisual content into discrete scenes by analyzing visual characteristics and temporal patterns, breaking down the complex processing task into manageable segments that maintain accuracy while reducing overall computational burden
Solution Approach 2:
The system applies different levels of analysis to different parts of the content, focusing detailed visual analysis only on key frames and representative scenes rather than uniformly processing all frames, thereby maintaining detection accuracy while reducing computational complexity
4Loss of information
If text extraction is performed on every frame, then character recognition completeness improves, but processing time increases
Solution Approach 1:
The system performs preliminary scene detection and frame selection before text extraction, identifying only the frames that contain relevant textual information, thereby ensuring character recognition completeness while minimizing processing time
Solution Approach 2:
The system extracts text only from selected key frames and scenes where textual content is likely to appear, rather than processing every frame, maintaining recognition completeness by focusing on informative regions while reducing overall processing time
Data Source
AI summary
Techniques are disclosed for automatic scene detection and character extraction. In one example, audiovisual content with video frames, an audio recording, and timing information is received. A score, based on the frame's visual characteristics, is determined for a first frame and subsequent frames. The first frame's score and subsequent frame's scores are compared to determine if the difference between the scores is above a threshold. When the difference in scores is above a threshold, the subsequent frame is classified as a new scene. The audiovisual content is segmented into scenes and textual characters are identified in at least one frame from each scene. The characters are stored and indexed in a searchable database with the timing information for the scene where the characters were identified. The audio recording is transcribed and the transcribed words are stored and indexed in the searchable database with timing information.