AI Intruder Detection System for Low False Positives
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Solution Overview
Problem
Existing intruder detection systems face challenges with accuracy and speed, often resulting in high false positives and low true positives, and are not effective in detecting intruders without manual intervention across various camera feeds.
Innovation Solution
A system and method utilizing a combination of artificial intelligence and online multi-frame logic, which includes a buffer zone for instant detection, agnostic functionality to various camera feeds, and a detection processing module to enhance accuracy and speed of intruder detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If motion detectors are used to detect intruders, then detection speed is improved, but false positive rate increases due to motion of pets, air currents, etc.
Solution Approach 1:
The patent combines multiple detection approaches (motion detection, vision-based detection, and behavioral analysis) into a unified system. By merging these different detection methods, the system achieves both fast response times and high accuracy, resolving the contradiction between detection speed and false positive rate.
Solution Approach 2:
The system implements feedback mechanisms where detection results from multiple sources are continuously evaluated and adjusted. The AI engine learns from false positives and refines detection algorithms, reducing false alarms while maintaining fast detection capability through iterative improvement.
2Measurement precision
If vision-based surveillance systems are deployed to monitor areas, then detection accuracy is improved, but device complexity and deployment cost increase
Solution Approach 1:
The system employs AI engines that automatically configure and optimize surveillance parameters without requiring manual intervention for each camera setup. The system self-adjusts detection zones, sensitivity levels, and camera configurations, reducing deployment complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent creates a universal surveillance platform that works across multiple camera types and environments. The AI engine adapts to different camera feeds (RGB, IR, various resolutions) and automatically configures detection parameters, eliminating the need for camera-specific configurations and reducing overall system complexity.
3Reliability
If multiple tubelets are logged for plurality of intruders in each frame, then detection completeness is improved, but processing time and resource consumption increase
Solution Approach 1:
The system extracts only the essential detection data from each frame and logs only critical information. By taking out only the necessary tubelet data and filtering out redundant information, the system maintains complete tracking of multiple intruders while significantly reducing processing time and resource consumption.
Solution Approach 2:
The system discards redundant detection data that can be reconstructed later and recovers only essential information for logging. This selective discarding and recovering approach allows complete multi-intruder detection while minimizing the data that needs to be processed and stored.
4Device complexity
If point of entry monitors are used to detect intruders, then system simplicity is improved, but detection reliability decreases when intruders bypass entry points
Solution Approach 1:
The patent transitions from single-point entry monitoring to multi-dimensional area surveillance using vision-based systems. By adding spatial dimensions and using AI-powered image analysis, the system detects intruders anywhere in the monitored area, not just at entry points, thereby improving reliability while maintaining reasonable system simplicity.
Data Source
AI summary
The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating intruder detection that is highly accurate especially for enhancing critical security zones, the intruder detection system (IDS) can provide high true positives and extremely low false positives, the system may be developed using techniques from artificial intelligence and computer vision and is focused on highly optimized intruder detection with equal attention paid to both accuracy and speed, the area of use is highly diverse and can be used mainly for surveillance and security purposes, be it right from outdoor areas such as perimeter walls, campuses to indoor areas such as malls, factory floors, and the like.