LiDAR Throw Detection for Cross-Checkpoint Security
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Solution Overview
Problem
Existing security systems struggle to efficiently detect and respond to objects being thrown across security checkpoints, particularly in areas like airports and train stations, where individuals attempt to smuggle contraband by throwing items from unsecured to secured zones.
Innovation Solution
A LiDAR-based security system with AI-enhanced object tracking and threat detection, capable of identifying throwing motions, tracking objects, and integrating with surveillance systems to alert personnel and automate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security monitoring systems are used, then the system complexity is low, but the detection precision of thrown objects is insufficient
Solution Approach 1:
The patent combines multiple LiDAR sensors with video management systems and artificial intelligence algorithms into an integrated security monitoring system. The LiDAR sensors detect thrown objects by analyzing point cloud data for abnormal motion patterns, while the VMS provides video verification, creating a multi-modal detection system that achieves high precision without requiring overly complex individual components
Solution Approach 2:
The patent introduces AI-powered anomaly detection algorithms as an intermediary between raw LiDAR point cloud data and security personnel. These algorithms automatically identify throwing motions by detecting abnormal arm movements and object trajectories, filtering out false positives and presenting only relevant threats to operators, thereby achieving high detection precision with manageable system complexity
2Loss of time
If manual surveillance is used, then the device complexity is low, but the response time to thrown objects is delayed
Solution Approach 1:
The patent implements continuous real-time monitoring of the security area using LiDAR sensors that constantly scan and analyze the point cloud environment. The AI algorithms are continuously analyzing arm motions and object trajectories before threats materialize, enabling the system to detect and alert security personnel about thrown objects immediately upon detection rather than relying on periodic manual checks
Solution Approach 2:
The patent replaces manual surveillance with an automated system comprising LiDAR sensors, point cloud processing algorithms, and AI-powered anomaly detection. This substitution eliminates human response delays by providing automated real-time detection and alerting, achieving rapid response times while managing device complexity through modular system architecture
3Adaptability or versatility
If comprehensive area coverage is implemented, then the detection capability improves, but the device complexity increases
Solution Approach 1:
The patent divides the security area into multiple zones monitored by individual LiDAR sensors positioned at strategic locations. Each sensor covers a specific sector and independently analyzes point cloud data for throwing motions. The system segments the monitoring task across multiple sensors rather than requiring one complex omnidirectional system, achieving comprehensive coverage with manageable individual component complexity
Solution Approach 2:
The patent designs the LiDAR-based detection system to perform multiple functions: detecting thrown objects, tracking object trajectories, identifying throwing motions through arm motion analysis, and integrating with existing video management systems. This multi-functional approach achieves comprehensive detection capability using a unified technology platform rather than requiring separate specialized systems for each function
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances real-time security by automating surveillance, ensuring continuous threat tracking, improving response times, and preventing contraband smuggling through accurate detection and alerting mechanisms.
Implementation Method 1
one or more LiDAR sensors generating a real-time three-dimensional (3D) point cloud representation of a security checkpoint area
Data Source
AI summary
The inventive throw detection system includes a plurality of lidars positioned to cover a predetermined security area, each lidar being wirelessly connected to a computer running a program to monitor the predetermined security area. The program detects when a person throws an object from an unsecured zone to a secured zone, and sends an alert to security.