Video Relationship Graphs for Anomalous Interaction Detection
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Solution Overview
Problem
Evaluating the strength of relationships between individuals, particularly for those with impairments affecting verbal and non-verbal communication, is difficult and often requires expertise in social dynamics.
Innovation Solution
A system and method using computer-implemented machine learning models to analyze digital video data, creating relationship graphs that identify interactions between individuals and detect anomalous edges indicative of unusual or negative changes in relationships, allowing for automated detection and recommendation of adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning models are used to analyze video data and detect relationship anomalies, then productivity and accessibility are improved, but device complexity increases
Solution Approach 1:
The system segments the complex relationship evaluation task into distinct functional modules: video data acquisition, feature extraction through machine learning, relationship graph construction, and anomaly detection. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving high productivity in relationship evaluation.
2Ease of operation
If automated machine learning models are used to analyze video data and detect relationship anomalies, then ease of operation is improved for individuals with impairments, but device complexity increases
Solution Approach 1:
The system enables individuals with impairments to independently evaluate relationships by providing automated analysis tools that do not require expert knowledge of social dynamics. The machine learning models perform the complex interpretation work automatically, allowing users to simply observe interactions and receive objective relationship strength assessments without needing specialized training.
3Measurement precision
If detailed video analysis is performed to evaluate relationship strength, then measurement precision is improved, but loss of time increases due to processing requirements
Solution Approach 1:
The system performs preliminary feature extraction from video data, identifying key interaction patterns and behaviors before conducting the full relationship analysis. By pre-processing the video content to extract relevant features such as body language, facial expressions, and interaction frequency, the system reduces the computational burden of subsequent analysis, achieving both high measurement precision and reduced processing time.
Data Source
AI summary
A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.