Video Analysis System Using Key Frame Segmentation
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Solution Overview
Problem
Current video analysis methods rely on user-provided tags, which can be misleading, and require full video runtime for accurate genre or category identification, leading to ineffective user experiences and inefficient processing.
Innovation Solution
A system that splits video files into shorter intervals, identifies key frames using comparators and pre-populated databases, and employs supervised and unsupervised reinforcement learning to automatically tag and index videos quickly, determining genre and category without relying on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video analysis is performed using full runtime processing, then measurement precision of video genre and context is improved, but loss of time increases significantly
Solution Approach 1:
The video file is divided into multiple segments or clips of fixed duration (e.g., 5-10 seconds each). The system analyzes only these representative segments rather than the entire video, thereby reducing analysis time while maintaining genre identification accuracy through strategic selection of informative segments.
Solution Approach 2:
The system performs preliminary analysis by extracting and analyzing video segments before full processing would occur. By pre-identifying key segments that represent the video's content, the system prepares data that enables rapid genre classification without requiring complete video processing.
2Productivity
If automated video analysis system is implemented, then productivity of video tagging is improved, but device complexity increases
Solution Approach 1:
The automated analysis system processes video files by dividing them into manageable segments, which reduces computational complexity for each processing unit while maintaining overall productivity through parallel or sequential processing of multiple segments.
Solution Approach 2:
The system extracts only essential visual features and key frames from video segments, discarding redundant data. This extraction approach reduces the complexity of subsequent analysis by focusing computational resources on the most informative elements.
3Ease of operation
If user-provided tags are used for video indexing, then ease of operation is improved, but reliability of video categorization deteriorates
Solution Approach 1:
The system automatically generates video tags and genre classifications by analyzing video content itself, eliminating dependence on user-provided metadata. This self-service approach maintains ease of operation for users while significantly improving reliability by base tagging on actual video content rather than subjective user input.
Data Source
AI summary
A system for analyzing a video file in a shortened time frame, said system comprising: a receiver (VFR) to receive a video file (VF) as an input; a Time Splitter (TR) to split the received video file according to set intervals of time depending on how fast said video is to be analyzed; a Frame splitter (FP) to split a Video Viewing Program (R) into a plurality of frames (F1, F2, . . . Fn); a Key Frame Identification mechanism (KFI) to identify key frames; and linking mechanism (LM) to cause a link to be formed with pre-populated databases and in-house libraries of images (D), of frames, which frames comprising associated tags, thereby determining a score of similar tags per video file in order to determine said genre, thereby determining multiplicity of instances for said time splitter.