Video Storage Retrieval Timeline Segmentation
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Solution Overview
Problem
Current video storage and retrieval systems face challenges in efficiently locating and retrieving specific video images or segments, especially for long-duration recordings, due to labor-intensive metadata tagging and limited retrieval criteria.
Innovation Solution
A video storage and retrieval system that employs electronic tagging of video images by timecode, timeline display for segment marking, and retrieval using a marker or cursor, allowing for multiple timelines and filters to locate specific events, including motion and telemetry detection, facilitating quick and precise image selection and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual metadata tagging is applied to video records to facilitate retrieval, then retrieval accuracy is improved, but labor time and cost increase significantly
Solution Approach 1:
The system performs automatic metadata tagging and index creation during the video recording process itself, rather than requiring post-recording manual tagging. This preliminary automated action prepares the video data for rapid retrieval without requiring operators to manually tag each video segment afterward, thus improving retrieval accuracy while eliminating the time-consuming manual labor.
2Adaptability or versatility
If comprehensive metadata tagging is applied to all video images, then retrieval capability is improved, but system complexity and processing time increase
Solution Approach 1:
The system applies metadata tagging selectively to specific portions of video records that contain events of interest, rather than uniformly tagging all video images. This local quality approach tags only the relevant segments with appropriate metadata, improving retrieval capability for specific events while reducing overall system complexity and processing requirements compared to comprehensive universal tagging.
3Measurement precision
If manual searching through video records is performed to locate specific segments, then retrieval precision is maintained, but productivity decreases
Solution Approach 1:
The system replaces manual mechanical searching through video records with an automated computer-based search system that uses metadata indexes and keywords. This substitution allows the system to rapidly locate specific video segments by automatically querying the metadata database and retrieving precise timecodes, thereby maintaining high retrieval precision while dramatically improving productivity and reducing the time required to locate specific content.
4Ease of manufacture
If video records of long duration are stored without segmentation, then storage simplicity is maintained, but retrieval efficiency deteriorates
Solution Approach 1:
The system automatically segments long-duration video records into smaller chronological segments or scenes, organizing them with hierarchical metadata structures. This segmentation divides the large video database into manageable units that can be independently indexed and searched, maintaining storage simplicity through automated organization while dramatically improving retrieval efficiency by allowing targeted searches within specific segments rather than scanning entire long-duration recordings.
Data Source
AI summary
A video image storage and retrieval system providing computer displays of timelines for video sequences, in which a first timeline shows time divisions for segments for the longer sequences, and the other timelines each provide for locating images within the segments. Timecodes in the other timelines are aligned with those in the first timeline so that searching for images in the other timelines is facilitated.