Shot Transition Detection for Video Content Retrieval
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Solution Overview
Problem
The sheer volume of video data and difficulty in identifying relevant portions hinder efficient analysis, leading to false positives and inefficiencies in detecting illegal content, copyright infringements, and object recognition, as existing tools are often unsuitable for comprehensive video analysis.
Innovation Solution
A system that detects shot transitions in videos to decompose content into manageable units, using frame-by-frame analysis and permutation metrics to accurately identify shot boundaries, enabling efficient indexing and subsequent analysis, such as object recognition and copyright detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video content is analyzed frame-by-frame using existing tools, then detection accuracy improves, but processing time becomes prohibitively long
Solution Approach 1:
The patent segments video content into shots using shot transition detection, then analyzes only key frames within each shot rather than every frame. This segmentation approach maintains detection accuracy by ensuring representative frames are analyzed while dramatically reducing processing time by avoiding redundant analysis of continuous frames within the same shot.
Solution Approach 2:
The patent performs preliminary shot transition detection and video decomposition before applying content analysis tools. By pre-identifying shot boundaries and selecting representative frames in advance, the system prepares the video data in a optimized format that enables accurate detection without requiring exhaustive frame-by-frame processing.
2Reliability
If analysis tools are applied to entire videos, then comprehensive detection is achieved, but false positives increase
Solution Approach 1:
The patent divides videos into shots and analyzes each shot independently, then aggregates results. This segmentation prevents false positives by ensuring that content is evaluated in its proper contextual boundaries, avoiding misinterpretation that can occur when analyzing continuous video streams without structural breaks.
Solution Approach 2:
The patent applies different analysis strategies to different shots based on their characteristics. By adapting the analysis approach to local shot properties rather than applying a uniform analysis to the entire video, the system improves detection reliability while reducing false positives from inappropriate analysis applications.
3Reliability
If video data is processed without decomposition, then all content is analyzed, but computational resources are wasted on duplicate and redundant content
Solution Approach 1:
The patent segments videos into shots and identifies duplicate shots through comparison, then processes only unique shots. This approach ensures analysis completeness by maintaining all unique content while eliminating computational waste from processing duplicate segments multiple times.
Solution Approach 2:
The patent identifies and discards duplicate shots from further processing after initial identification, while preserving the information that duplicates exist. This discarding of redundant content for processing while maintaining analysis completeness significantly reduces computational resource consumption.
Data Source
AI summary
Various disclosed embodiments relate to video content analysis based in part upon the detection of shot transitions. In some embodiments, a process and computer system for detecting shot transitions in a video is used to separate a video sequence into a series of “shots” having multiple frames. These shots may then be used for additional processing, e.g., content detection within the video frames.