UAV Premises Security Analytics with Distributed ML
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Solution Overview
Problem
Current premises security systems face challenges in effectively and efficiently responding to anomalies, such as fires or burglaries, especially when communication with on-site personnel is unreliable, and there is a need for enhanced surveillance capabilities to support timely and accurate decision-making for first responder dispatch.
Innovation Solution
A distributed video surveillance system incorporating unmanned aerial vehicles (UAVs) and analytics devices that communicate through a network, allowing UAVs to autonomously respond to security alerts, gather video surveillance, and process data using machine learning models for object detection and threat assessment, with a remote monitoring system verifying alarms and dispatching appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional premises security systems rely on contacting on-site personnel to verify anomalies, then communication reliability is required, but response time is delayed when communication fails
Solution Approach 1:
The patent introduces an aerial vehicle as an intermediary component between the premises security system and the remote monitoring center. When an anomaly is detected, the system automatically deploys the aerial vehicle to visually verify the condition at the premises, eliminating the need to wait for on-site personnel confirmation. This intermediary approach resolves the contradiction by providing a reliable verification method that does not depend on communication with on-site personnel, thereby reducing response time while maintaining reliability.
2Measurement precision
If aerial vehicles are deployed to verify every anomaly, then surveillance accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements parameter changes by introducing confidence thresholds and anomaly severity levels that determine whether aerial vehicle deployment is triggered. Not every anomaly automatically deploys an aerial vehicle; instead, the system evaluates the nature and confidence of the detected anomaly against predefined parameters. This selective approach maintains high surveillance accuracy for critical cases while avoiding unnecessary complexity and cost for minor or low-confidence detections.
3Reliability
If manual verification by remote monitoring center personnel is used, then false alarms can be identified, but labor costs and verification time increase
Solution Approach 1:
The patent implements self-service by enabling the security system to automatically verify anomalies using aerial vehicle surveillance and AI-powered image analysis. The system independently determines whether detected anomalies represent true threats or false alarms without requiring manual intervention from remote monitoring center personnel. This automation maintains reliable false alarm identification while dramatically improving verification efficiency and reducing labor costs.
4Speed
If first responders are dispatched without verification, then response speed is maximized, but unnecessary dispatches waste resources
Solution Approach 1:
The patent applies preliminary action by deploying aerial vehicles to verify anomalies before triggering first responder dispatch. The system performs preliminary surveillance and analysis to confirm that detected anomalies represent genuine threats requiring emergency response. This preliminary verification step maintains fast response speeds for legitimate threats while preventing unnecessary dispatches for false alarms, thereby avoiding resource waste.
Data Source
AI summary
A method implemented by a system for security comprising an unmanned aerial vehicle (UAV) and an analytics device configured to communicate with a remote monitoring system and the UAV is provided. Media data from the UAV is received at an analytics device, where the media data includes surveillance information corresponding to a premises under surveillance. Security attributes associated with the premises are detected based at least in part on a first level of machine learning (ML) analysis performed on the media data. The media data are transmitted by the analytics device to the remote monitoring system for a second level of ML analysis based at least in part on the security attribute, where the first level of ML analysis is less computationally expensive compared to the second level of ML analysis, and the second level of ML analysis is performed at the remote monitoring system on the media data.