Layered Architecture for Dynamic Social Network Construction from Image Data
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Solution Overview
Problem
Current video observation systems at security facilities, such as airports, are unable to detect security threats and lack the capability to construct a social network from image data, limiting their effectiveness in monitoring and analyzing surveillance video.
Innovation Solution
A system with a layered architecture that constructs a dynamic social network from image data by processing raw video analytics into an actor-event matrix, which is then used to infer relationships and build a social network, incorporating importance weights for events and using algorithms for biometric identification and motion tracking across multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current video observation systems are used for monitoring facilities, then basic monitoring functions are achieved, but the systems are unable to detect security threats and construct social networks from image data
Solution Approach 1:
The system segments the complex task of security analysis into multiple processing layers: low layer handles raw image data and video analytics, middle layer processes actor-event matrices, and high layer constructs social networks. This segmentation enables each layer to specialize in specific functions, improving both threat detection reliability and social network construction capability simultaneously
Solution Approach 2:
The patent introduces an intermediary actor-event matrix that bridges raw video data and social network construction. This matrix serves as a mediator that transforms unstructured image data into structured relationship information, enabling the system to detect security threats while building social networks from the same data source
2Productivity
If raw video analytics data is processed directly without structured organization, then processing speed is maintained, but the system cannot effectively infer relationships or construct social networks
Solution Approach 1:
The system performs preliminary organization of video analytics data into an actor-event matrix structure before social network construction. This preliminary action pre-processes the data into a format that preserves relationship information while maintaining processing efficiency, as the structured matrix enables faster query and analysis operations compared to unstructured data
3Loss of information
If a comprehensive social network is constructed from all observed data, then complete relationship analysis is achieved, but the system complexity increases significantly
Solution Approach 1:
The patent divides the social network construction process into distinct layers with specific responsibilities: the low layer manages data acquisition and basic analytics, the middle layer handles matrix operations and relationship inference, and the high layer performs social network construction and analysis. This segmentation reduces system complexity by localizing functions while maintaining comprehensive relationship analysis capability
Solution Approach 2:
The system transitions from two-dimensional video frames to a three-dimensional actor-event-relationship space, adding the dimension of temporal and relational context. This dimensional transformation enables comprehensive relationship analysis by organizing data in a multi-dimensional matrix structure that captures interactions across time and space without linearly increasing system complexity
Data Source
AI summary
A system having a layered architecture for constructing dynamic social network from image data of actors and events. It may have a low layer for capturing raw data and identifying actors and events. The system may have a middle layer that receives actor and event information from the low layer and puts it in to a two dimensional matrix. A high layer of the system may add weighted relationship information to the matrix to form the basis for constructing a social network. The system may have a sliding window thus making the social network dynamic.