Video Data Condensing for Surveillance Storage Optimization
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Solution Overview
Problem
Surveillance cameras generate large amounts of video data, leading to storage space issues and inefficiencies in identifying relevant information due to redundant data, making it difficult to quickly find effective information.
Innovation Solution
A data processing method that synthesizes a target image sequence of a target object with a background image, extracted from original video data, to create condensed video data, removing redundant information and retaining only the object of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all-day video data is stored for recording and viewing history information, then complete surveillance coverage is achieved, but storage space is excessively occupied and storage costs increase
Solution Approach 1:
The patent extracts and removes redundant background information from video data, keeping only the essential target object information. This extraction process reduces storage requirements while maintaining the core surveillance function by storing only meaningful content rather than complete video streams.
Solution Approach 2:
The patent applies different processing qualities to different parts of video data - target objects are preserved with high quality and detail, while background areas are simplified or removed. This local differentiation allows reduced storage space while maintaining surveillance effectiveness for important elements.
2Reliability
If all-day video data is stored for complete recording, then no information is lost, but browsing and identifying effective information takes a long time
Solution Approach 1:
The patent extracts only the relevant target object information from complete video data, removing unnecessary background content. This allows rapid browsing and identification of effective information while maintaining reliability by preserving all target object appearances throughout the surveillance period.
Solution Approach 2:
The patent segments video data into essential target object information and redundant background information, storing only the segmented essential portions. This segmentation enables quick access to meaningful data without sacrificing information completeness about target objects.
3Loss of information
If complete video data is retained for analysis, then all information is available, but the amount of redundant information makes it difficult to identify target objects quickly
Solution Approach 1:
The patent extracts and retains only the essential target object information from complete video data, removing redundant background information. This extraction maintains information retention for target objects while dramatically improving target identification efficiency by eliminating distracting redundant content.
Solution Approach 2:
The patent applies selective quality retention - target objects are preserved with complete information while background areas are removed or simplified. This local quality differentiation maintains necessary information for target identification while improving overall processing efficiency.
Data Source
AI summary
A method includes: obtaining video data; receiving a target object; processing the video data to obtain one or more first images of the video data that contain an image of the target object and one or more second images of the video data that do not contain the image of the target object; discarding the one or more second images from the video data; generating a background image based on the one or more second images; synthesizing the one or more first images of the video data with the background image to generate one or more synthesized images; obtaining a target image sequence with the one or more synthesized images based on a time order of the one or more first images in the video data; and displaying each of the one or more synthesized images in the target image sequence in a fade-in and fade-out manner.