Adaptive Subject Identification Synchronization Server
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Solution Overview
Problem
The challenge of identifying a same subject across multiple cameras and/or multiple different face recognition systems increases with the increased number of camera deployments, as each system maintains its own database and lacks a unified method for synchronizing subject identifications.
Innovation Solution
A server system is introduced that can adaptively update target subject identifications stored in a database by receiving requests for synchronization from other servers, comparing the received information against the stored data, and determining whether to accept the request to synchronize the target subject identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If each face recognition system maintains its own database independently, then each system can operate autonomously and process local identification tasks efficiently, but the ability to identify the same subject across multiple cameras and systems deteriorates as the number of deployments increases
Solution Approach 1:
The system divides the database management into two segments: local databases that store and process identification data autonomously for fast local operations, and a centralized server database that coordinates cross-system synchronization. This segmentation allows local systems to maintain high identification efficiency while the centralized server ensures cross-system accuracy through unified subject identification management.
2Area of stationary object
If the number of camera deployments increases, then the coverage and surveillance capability improve, but the difficulty of synchronizing subject identifications across multiple systems increases
Solution Approach 1:
A centralized server acts as an intermediary between multiple face recognition systems. The server receives subject identification data from various cameras and systems, performs centralized matching and synchronization, then distributes unified identification results back to the local systems. This intermediary approach enables expanded surveillance coverage while managing synchronization complexity centrally rather than requiring complex peer-to-peer coordination between all systems.
3Reliability
If a centralized database is implemented to synchronize subject identifications across all systems, then cross-system identification accuracy improves, but the system complexity and data management burden increase
Solution Approach 1:
The patent merges the database functions of multiple distributed systems into a centralized database server. Instead of each system maintaining separate databases requiring complex synchronization protocols, all subject identification data is consolidated into a single centralized repository. This merging approach ensures consistent subject identification across all systems while simplifying data management, as the centralized server handles all data storage, retrieval, and synchronization operations through unified protocols.
Data Source
AI summary
Present disclosure provides a method and a server for adaptively updating a target subject identification to be stored in a database, the target subject identification pertaining to a target subject in a system comprising a plurality of servers, the server configured to: receive target subject information; assign a first target subject identification to the target subject information and store the first target subject identification in a first database of the server; and send a request, to the plurality of servers, to synchronize the target subject identification, the request including the target subject information relating to the target subject.