Machine Learning Model for Restricted Area Detection and Deterrence
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Solution Overview
Problem
Existing security systems that use cameras struggle to effectively monitor and prevent unauthorized access to restricted areas without constant human supervision.
Innovation Solution
The implementation of a system that utilizes machine-learning models to analyze data from various sensors, including cameras, to detect individuals and adjust monitoring levels or execute deterrence actions based on predefined security levels for different areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera footage is captured but not viewed by a user, then storage space is occupied, but the footage cannot be utilized to prevent harmful activities
Solution Approach 1:
The system enables security cameras to automatically analyze footage and detect harmful activities without requiring constant human supervision. The machine learning model processes video data in real-time, allowing the system to serve itself by identifying and responding to security threats autonomously.
Solution Approach 2:
The patent replaces manual review of camera footage with an automated machine learning-based video analysis system. This substitution transforms the mechanical process of human viewing and decision-making into an automated computational process that continuously monitors and responds to security events.
2Reliability
If a homeowner constantly monitors security camera feeds, then harmful activities can be identified, but it becomes impractical due to time and attention requirements
Solution Approach 1:
The security system performs self-monitoring through automated video analysis, eliminating the need for homeowners to constantly watch camera feeds. The machine learning model independently processes video data, detects threats, and triggers appropriate responses without requiring continuous user attention or intervention.
Solution Approach 2:
The system replaces the manual monitoring process with an automated machine learning-based detection system. This substitution eliminates the impractical burden of constant human supervision while maintaining or improving detection accuracy through continuous automated analysis of video feeds.
3Productivity
If machine-learning models automatically analyze camera footage, then real-time detection is achieved, but system complexity increases
Solution Approach 1:
The patent replaces manual security monitoring with an automated machine learning-based video analysis system. This substitution increases productivity by enabling continuous real-time detection without human intervention, while the complexity is managed through integration of specialized AI models designed for security applications.
Data Source
AI summary
Systems and methods are disclosed for detecting, by a computer executing a machine-learning model, a person, and determining, by the computer executing the machine-learning model, that the person is within a first area, in response to determining that the person is within the first area, adjusting, by the computer executing the machine-learning model, a level of monitoring, determining, by the computer executing the machine-learning model, that the person is within a second area, in response to determining that the person is within the second area, executing, by the computer executing the machine-learning model, a deterrence action, wherein the machine-learning model is trained by applying the machine-learning model on historical data.