Machine Learning Threat Detection for Multi-Feed Surveillance
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Solution Overview
Problem
Existing security and surveillance systems struggle with real-time monitoring of multiple video feeds, leading to missed events of interest due to human limitations in reviewing vast amounts of video data, compromising security and safety.
Innovation Solution
A machine learning-based system that integrates sensor data from various sources, performs real-time feature extraction and detection, and generates contextual event descriptions using a multi-feature detection ensemble, enabling proactive threat detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators review video feeds in real-time, then security events can be detected, but the number of events that can be monitored is limited by the number of available security personnel
Solution Approach 1:
The patent replaces the mechanical system of human operators watching video feeds with an automated machine learning-based computer vision system. The system uses trained models to automatically detect security events in video feeds, eliminating the need for human operators to manually review each feed while significantly increasing the number of feeds that can be monitored simultaneously.
Solution Approach 2:
The patent introduces an intermediary automated detection system that sits between the video feeds and human operators. This intermediary system pre-processes video feeds, automatically detects security events, and only presents relevant alerts to human operators, thereby amplifying the monitoring capacity without requiring proportional increases in human personnel.
2Area of stationary object
If multiple video cameras are deployed to cover defined spaces, then surveillance coverage is improved, but the complexity of monitoring and reviewing all video feeds increases
Solution Approach 1:
The patent segments the complex task of monitoring multiple video feeds by deploying distributed edge computing devices at different locations. Each edge device independently processes video feeds from local cameras, performing feature extraction and event detection locally. This segmentation distributes the computational complexity across multiple devices rather than requiring a single centralized system to process all feeds.
Solution Approach 2:
The patent adds a spatial dimension to the monitoring architecture by distributing processing across multiple edge devices located throughout the monitored area. Instead of centralizing all video feeds at one location, the system processes video data at the edge of the network closest to where the events occur, reducing data transmission requirements and localizing complexity management.
3Measurement precision
If extensive video data is captured for security monitoring, then detection accuracy can be improved, but the time required to review the data increases
Solution Approach 1:
The patent performs preliminary processing of video data by extracting features and detecting events in real-time as video feeds are captured, rather than reviewing all captured data after the fact. The system continuously analyzes video streams, identifies security events as they occur, and generates alerts immediately, eliminating the need for subsequent manual review of extensive video data archives.
Solution Approach 2:
The patent extracts only the most relevant information from extensive video data by using machine learning models to identify and extract features indicative of security events. Instead of requiring human operators to review entire video feeds, the system extracts and presents only the critical event information, significantly reducing the time required for detection while maintaining high accuracy.
Data Source
AI summary
Systems and methods for implementing a threat model that classifies contextual events as threats.