Target Retrieval System Using Semi-Structured Feature Comparison
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Solution Overview
Problem
The existing personnel search process is labor-intensive and inefficient, as it relies on manual retrieval of structured information from historical databases and real-time video analysis, making it difficult to implement timely and accurate target identification.
Innovation Solution
A target retrieval method that acquires structured feature information, converts it into semi-structured feature information, and compares it with real-time video stream data to determine if a potential target matches the retrieval criteria, thereby reducing labor and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual retrieval of structured information from historical databases and real-time video analysis is used, then target identification can be performed, but labor consumption is high and retrieval efficiency is low
Solution Approach 1:
The system enables self-service by automatically performing target retrieval through feature information comparison. The retrieval system autonomously queries historical databases, analyzes real-time video streams, and identifies targets without requiring manual intervention, thereby eliminating labor consumption while maintaining high retrieval efficiency
Solution Approach 2:
The patent replaces the manual mechanical retrieval process with an automated information processing system. Instead of personnel manually searching databases and analyzing videos, the system uses computer-based automated querying, feature extraction, and comparison algorithms to perform the same function, significantly improving productivity
2Loss of time
If manual search of historical database and real-time video monitoring is performed, then target identification is possible, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by pre-extracting and storing feature information from historical video data in structured formats before actual retrieval is needed. When a retrieval request occurs, the system can immediately compare query features against pre-processed historical data without performing time-consuming analysis during the retrieval moment, thereby reducing retrieval time while improving efficiency
Solution Approach 2:
The system maintains continuity of useful action by continuously analyzing real-time video streams and updating feature information in the database. This continuous processing ensures that when retrieval is needed, the most current target information is already available for immediate comparison, eliminating delays associated with batch processing or manual review
Data Source
AI summary
Provided are a target retrieval method and device, and a storage medium. The target retrieval method includes acquiring structured feature information that is input when information retrieval is performed on a preset information retrieval database; acquiring the totality of semi-structured feature information within a first preset period and a first preset range from the information retrieval database according to time information and range information contained in the input structured feature information and using the semi-structured feature information as to-be-retrieved thermal data; acquiring real-time video streams of the complete set of cameras within a second preset period and a second preset range; acquiring semi-structured feature information of a potential target in the real-time video streams; and comparing the semi-structured feature information of the potential target with the semi-structured feature information in the thermal data and determining whether the potential target is a retrieval target according to the comparison result.