Video Segmentation Using Playback Behavior and Audio Cues
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Solution Overview
Problem
Existing video content segmentation methods, such as manually marking positions of opening credits, closing credits, and advertisements, are inefficient and costly.
Innovation Solution
A method that utilizes playback behavior data and audio features to automatically determine content segmentation points in videos, including opening credits end points, closing credits start points, and advertisement end points, by dividing videos into segments based on user interactions and extracting audio features for precise positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual marking methods are used to segment video content, then segmentation accuracy can be ensured, but processing efficiency is low and costs are high
Solution Approach 1:
The patent replaces the mechanical manual marking process with an automated system that combines playback behavior data analysis and audio feature extraction. The system automatically identifies content segmentation points by detecting user interaction patterns and analyzing audio characteristics, eliminating the need for manual video review while maintaining high segmentation accuracy.
Solution Approach 2:
The system enables self-service segmentation by utilizing the video's own playback behavior data and audio features to automatically determine segmentation points. The video content itself provides the necessary information through user interaction patterns and audio characteristics, allowing the system to segment without external manual intervention.
2Productivity
If automated segmentation methods are used, then processing efficiency is improved, but segmentation accuracy deteriorates
Solution Approach 1:
The patent divides the automated segmentation process into two distinct stages: first, using playback behavior data to identify candidate segmentation regions, and second, using audio feature extraction to precisely locate segmentation points within those regions. This multi-stage segmentation approach maintains high accuracy while achieving full automation and improved processing efficiency.
Solution Approach 2:
The patent introduces playback behavior data as an intermediary that bridges the gap between automated processing and accurate segmentation. By first analyzing user interaction patterns to identify promising regions, then applying audio feature analysis only to those regions, the system achieves both automation and precision without requiring full manual review of entire videos.
3Measurement precision
If full video analysis is performed to locate segmentation points, then segmentation accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary analysis of playback behavior data to identify candidate regions containing potential segmentation points before conducting detailed audio feature extraction. This preliminary filtering action narrows down the search space, allowing the system to focus computational resources only on relevant video segments and significantly reducing overall processing time while maintaining accurate segmentation.
Solution Approach 2:
Instead of analyzing the entire video uniformly, the patent applies detailed audio feature extraction only to specific candidate regions identified through playback behavior analysis. This partial action approach concentrates computational effort where it is most needed, achieving accurate segmentation without the time cost of processing the complete video duration.
Data Source
AI summary
A method is provided that includes: obtaining playback behavior data of a video to be processed; determining, based on the playback behavior data, a target video segment in which a content segmentation point of the video is located; extracting an audio feature of the target video segment; and determining the content segmentation point from the target video segment based on the audio feature.