Scalable Video Sequence Identification Using Multi-Dimensional Signatures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video processing technologies face challenges in efficiently indexing and identifying video sequences within large databases, particularly in handling high-definition formats and resisting content changes or distortions, which limits their scalability and accuracy in applications like copyright protection and surveillance.
Innovation Solution
The method involves determining active regions in video frames, extracting features, and generating multi-dimensional signatures based on temporal statistical characteristics and contour pixel gradients, allowing for efficient video sequence structuring and database formation, enabling robust and scalable video identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current video processing technologies are used for indexing and identifying video sequences in large databases, then basic video identification can be achieved, but scalability and accuracy are limited especially for high-definition formats and large database sizes
Solution Approach 1:
The patent segments video sequences into key frames based on temporal statistical characteristics, and further segments key frames into active regions based on spatial variability. This hierarchical segmentation enables efficient processing of large HD video databases while maintaining identification accuracy by focusing computational resources on the most informative portions of video content.
Solution Approach 2:
The patent transforms video identification from traditional single-dimensional approaches to multi-dimensional analysis by extracting features across temporal dimensions (frame sequences), spatial dimensions (active regions), and feature space dimensions (multiple video features). This dimensional expansion significantly improves scalability and accuracy for large database searching.
2Adaptability or versatility
If comprehensive video database indexing is implemented to handle large database sizes, then video search capability is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the most essential and informative portions of video content by identifying key frames through temporal statistical analysis and active regions through spatial variability measurement. This extraction approach reduces processing complexity by eliminating redundant information while maintaining comprehensive database indexing capability.
Solution Approach 2:
The patent performs preliminary processing by pre-identifying key frames and active regions before actual video identification queries. This preliminary action creates an optimized data structure that reduces computational complexity during search operations, enabling efficient handling of large video databases.
3Measurement precision
If traditional video identification methods are used, then basic functionality is maintained, but accuracy and robustness against video content changes are insufficient
Solution Approach 1:
The patent applies local quality analysis by focusing computational efforts on active regions within key frames that exhibit high spatial variability. Instead of uniformly processing entire frames, the method concentrates resources on locally significant areas, thereby improving identification accuracy while managing extraction complexity through selective processing.
Data Source
AI summary
Scaleable video sequence processing with various filtering rules is applied to extract dominant features, and generate unique set of signatures based on video content. Video sequence structuring and subsequent video sequence characterization is performed by tracking statistical changes in the content of a succession of video frames and selecting suitable frames for further treatment by region based intra-frame segmentation and contour tracing and description. Compact representative signatures are generated on the video sequence structural level as well as on the selected video frame level, resulting in an efficient video database formation and search.