Threshold-Based AI Threat Detection and Deterrent Deployment
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Solution Overview
Problem
Existing security systems are inefficient and prone to errors in detecting and responding to potential crime threats due to reliance on human supervision, and existing deterrents lack real-time deployment capabilities.
Innovation Solution
An artificial intelligence-augmented camera security system that processes video feeds to detect potential threats, evaluates their severity, and autonomously or manually deploys appropriate intervention measures, including non-lethal deterrents, based on deterrence scores and threshold levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human supervision is used to monitor and detect crime threats, then the system can identify potential threats, but the detection accuracy and response time are reduced due to human error and inability to timely respond
Solution Approach 1:
The system enables self-service by implementing automated AI-based threat detection and intervention deployment that operates independently without requiring continuous human supervision. The AI module autonomously analyzes video feeds, identifies potential threats, and triggers appropriate intervention measures, eliminating reliance on human operators for real-time crime prevention.
Solution Approach 2:
The patent replaces the mechanical system of human visual monitoring with an automated AI vision system. The AI module processes video feeds from cameras, detects potential threats using machine learning algorithms, and automatically deploys intervention measures, substituting human cognitive processing with automated computational analysis to improve both accuracy and response time.
2Productivity
If automated AI intervention is deployed, then response time and detection accuracy are improved, but the system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a single unified platform: video feed processing, threat detection using AI, automated intervention selection, and deployment of various intervention measures (audio warnings, visual deterrents, law enforcement alerts). This universal system handles diverse crime scenarios through a single automated architecture, managing complexity through functional integration rather than separate systems.
3Object-affected harmful factors
If traditional burglar alarms are used, then crime deterrence is attempted, but the effectiveness is reduced due to high false alarm rates
Solution Approach 1:
The system implements feedback by continuously analyzing video feeds and using AI to evaluate the authenticity of detected threats before triggering intervention measures. The AI module processes visual data, assesses the likelihood of actual criminal activity versus false alarms, and only activates interventions when threats are confirmed, thereby reducing false alarm rates while maintaining effective crime deterrence.
Data Source
AI summary
Provided herein are systems for crime detection, deterrence, and intervention and methods of use thereof. The crime detection, deterrence, and intervention system employs an artificial intelligence module to reliably monitor and detect when crimes are in progress. A request to deploy a deterrence or intervention measure is evaluated by the artificial intelligence module to determine whether the requested deterrence or intervention measure should be automatically approved, automatically denied, or sent for further review and approval.