Video Credit Roll Detection via Text Block Analysis
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Solution Overview
Problem
Unpredictable schedules for television and video programming often result in incomplete recordings due to events exceeding scheduled end times, leading to a cascade effect where user-defined recordings may not capture the entirety of a program.
Innovation Solution
A system and method for detecting the beginning of a credit roll in a video stream by analyzing frames for text blocks, using wavelet analysis to identify the transition point between substantive content and credit roll, allowing automatic extension of recordings and insertion of additional content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If recording is based on predefined scheduling information, then recording setup is simple, but recording completeness deteriorates when programs extend past scheduled end time
Solution Approach 1:
The system continuously monitors the video stream for credit roll detection and uses this feedback to dynamically adjust the recording end time, ensuring complete capture of programs that extend beyond their scheduled duration
Solution Approach 2:
The system performs preliminary analysis of video frames to detect credit rolls before they complete, allowing proactive extension of recording duration to capture the entire program including credits
2Measurement precision
If credit roll detection is performed by analyzing all frames, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system analyzes only selected frames at predetermined intervals rather than every frame, providing sufficient detection accuracy while significantly reducing processing time and computational resources required
Solution Approach 2:
The system performs periodic sampling of video frames at predetermined time intervals to detect credit rolls, balancing detection accuracy with efficient processing by not continuously analyzing every frame
3Reliability
If text blocks are identified in every frame, then text detection completeness improves, but computational complexity increases
Solution Approach 1:
The system identifies text blocks only in selected frames rather than every frame, maintaining sufficient text detection completeness for credit roll identification while reducing computational complexity
Solution Approach 2:
The system extracts and analyzes only the essential text block information needed for credit roll detection, removing unnecessary computational overhead from analyzing all visual elements in every frame
Data Source
AI summary
A text detection process may involve comparing high-contrast pixel densities of areas of images of a video to determine quantities of text-containing areas in the images. Based on a difference between quantities of text-containing areas of subsets of the images, an image of the video may be selected for modification.