Disruptor Mitigation via Behavior Parameter Analysis
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Solution Overview
Problem
Existing systems lack effective methods to detect and mitigate disruptive behavior in crowds, which can negatively impact events and the experience of other attendees.
Innovation Solution
A method and system that utilize data feeds from various sources, including video cameras and social media, to analyze behavior parameter values, identify disrupted groups, and detect disruptor individuals, providing mitigation outputs to address disruptive behavior through machine logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data feeds from multiple sources are collected and analyzed to detect disruptors, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the crowd monitoring task into multiple independent analysis modules, each processing specific behavior parameters from different data sources (video feeds, social media, sensor data). This allows parallel processing of multiple data streams without overwhelming the central system, improving detection accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including behavior parameter extraction modules and machine learning classifiers that act as mediators between raw data feeds and disruptor detection. These intermediaries process and filter data from multiple sources, transforming complex multi-source data into actionable behavior parameters that indicate disruptive activity.
2Speed
If real-time analysis of behavior parameters is performed on crowd data, then disruptor mitigation speed improves, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by continuously analyzing crowd data and establishing baseline behavior patterns before disruptive events occur. Machine learning models are pre-trained on historical crowd data to recognize normal vs. abnormal behavior patterns, enabling rapid real-time detection without requiring intensive computational resources during actual disruptor identification.
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on specific high-risk zones or individuals exhibiting suspicious behavior patterns, rather than analyzing every individual in the crowd uniformly. This selective approach maintains rapid response capability while reducing overall computational resource consumption by concentrating processing power where it is most needed.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining one or more data feed that includes data of individuals within a crowd, wherein the crowd comprises a plurality of individuals gathered within an area; examining data of the one or more data feed to return behavior parameter values for respective individuals of the plurality of individuals; identifying, using values of the behavior parameter values, a disrupted group of a plurality of the individuals of the crowd, wherein the identifying is in dependence on a first one or more criterion being satisfied; detecting, using values of the behavior parameter values, a disruptor individual within the disrupted group, the detecting in dependence on a second one or more criterion being satisfied; and providing, by machine logic, one more disruptor mitigation output to mitigate disruptive behavior of the disruptor individual.