Distributed Pattern Recognition Edge Nodes
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Solution Overview
Problem
Conventional video management systems face challenges in real-time pattern recognition and data sharing across disparate institutions, leading to inefficiencies in event analysis and response, particularly in cases like missing children or security incidents, where timely and accurate identification is crucial.
Innovation Solution
A distributed intelligent pattern recognition system that enables cooperative multi-agent detection using disjunctive devices like cameras, employing a bidirectional feedback mechanism for neural network learning, allowing for real-time updates and prioritization of algorithms based on object recognition tasks, and integrating with cloud services for wide-area pattern detection and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional video management systems store and analyze recorded data retrospectively at a command center, then data can be collected and stored centrally, but the system cannot provide real-time pattern recognition and response capabilities
Solution Approach 1:
The system divides the centralized video management architecture into distributed edge computing nodes deployed at multiple agencies and locations. Each edge node performs local pattern recognition and analysis, eliminating the need to transport all raw data to a central command center for retrospective analysis. This segmentation enables real-time processing while reducing central system complexity.
Solution Approach 2:
Pattern recognition algorithms and machine learning models are pre-deployed to edge computing devices at various agencies before incidents occur. These pre-configured systems can immediately analyze video data and detect patterns in real-time without waiting for central command center processing, significantly reducing response time for events like missing children or security incidents.
2Productivity
If video data from disparate institutions is analyzed retrospectively to understand events, then data from multiple sources can be obtained, but the analysis process is slow and inefficient
Solution Approach 1:
Machine learning models and pattern recognition algorithms are pre-trained and deployed to edge devices before incidents occur. When events happen, these pre-configured systems can immediately analyze video data from multiple sources in real-time, dramatically improving event analysis efficiency compared to retrospective analysis of collected data.
Solution Approach 2:
The system implements feedback loops where analysis results from one incident are used to continuously improve and update pattern recognition models. This feedback mechanism allows the system to learn from each event, improving future analysis efficiency and accuracy while maintaining the value of information across disparate institutional data sources.
3Reliability
If human recipients are expected to remember images and information of missing children, then simple electronic alert systems can be used, but human memory fades and information loses value over time
Solution Approach 1:
The system replaces the mechanical human memory system with automated electronic pattern recognition and machine learning models deployed at edge computing nodes. These electronic systems can store and compare images indefinitely without degradation, continuously analyzing video feeds to detect missing children even weeks or months after they went missing, eliminating the fading memory problem inherent in human-based systems.
4Area of stationary object
If stolen children are moved significant distances away from the location they were taken, then the search area must be expanded, but the time required to locate them increases
Solution Approach 1:
Pattern recognition models are pre-deployed to edge computing devices across a wide geographic area before incidents occur. When a child is taken, these pre-configured systems can immediately begin analyzing video feeds from locations hundreds of miles away simultaneously, dramatically reducing the time required to locate children even when they are moved significant distances from their abduction location.
Data Source
AI summary
Embodiments include a system, method, and computer program product for distributed intelligent pattern recognition. Embodiments include a cooperative multi-agent detection system that enables an array of disjunctive devices (e.g., cameras, sensors) to selectively cooperate to identify objects of interest over time and space, and to contribute an object of interest to a shared deep learning pattern recognition system based on a bidirectional feedback mechanism. Embodiments provide updated information and/or algorithms to one or more agencies for local system learning and pattern updating recognition models. Each of the multiple agencies may in turn, update devices (e.g., cameras, sensors) coupled to the local machine learning and pattern recognition models.