Event Aggregation System for Surveillance Escalation Prediction
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Solution Overview
Problem
Surveillance systems face challenges in determining whether an abnormal event will escalate into a serious one, requiring users to manually review video footage, which is time-consuming and inefficient.
Innovation Solution
A computer-implemented method and system that uses sensors and processors to receive event information, collect and analyze data from various sources, including surveillance cameras and social media, to determine if an event will aggravate, categorizing factors as escalating, neutral, or mitigating, and providing alerts or resource recommendations to security teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review video footage to determine event escalation, then judgment accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary analysis by collecting information around the event location (crowd density, traffic conditions, weather, social media data) and categorizing factors as escalating, neutral, or mitigating before the event actually escalates. This advance preparation enables faster decision-making when events occur, reducing the need for time-consuming manual video review while maintaining accurate judgment through pre-processed contextual data.
Solution Approach 2:
The system introduces an intermediary analytical layer that processes video footage and contextual information automatically, generating event aggregation assessments that assist users. This intermediary system handles the time-consuming analysis work, allowing users to make accurate judgments based on pre-synthesized information rather than manually reviewing raw video footage.
2Reliability
If surveillance system focuses only on abnormal event detection, then detection capability is improved, but ability to predict escalation deteriorates
Solution Approach 1:
The system segments information gathering into multiple independent components: event detection from video footage, contextual data collection (crowd density, traffic, weather), and social media monitoring. Each segment focuses on specific aspects, allowing the system to maintain strong event detection capability while simultaneously gathering comprehensive contextual information that enables escalation prediction.
Solution Approach 2:
The system transitions from two-dimensional event detection (what is happening) to three-dimensional analysis by adding the temporal dimension of escalation prediction (what will happen). By incorporating contextual information about surrounding environment and historical patterns, the system gains predictive capability while preserving its core detection function.
Data Source
AI summary
A computer-implemented method for determining whether or not an event is going to aggravate using a sensor operationally coupled to a processor is disclosed. The method includes receiving, from the sensor, event information identifying a type of the event and location information identifying a location at which the event is occurring, collecting, from the sensor, information around the location in response to the location information, analyzing, at the processor, the information around the location, and determining whether or not the event is going to aggravate, at the processor, based on a result of the analysis.