AWS Vehicle Security Control
EXPERTISE
Cloud Computing
YEAR
2024
The project aimed to develop an AI-powered vehicle security control system that automates the detection and verification of vehicles at a security checkpoint. By leveraging Amazon Web Services (AWS), the system employs advanced image recognition, real-time processing, and a secure workflow to streamline the entry process while enhancing security measures.
Timeline
The system was conceptualized, designed, and implemented over a 10-week period, with iterative development phases focusing on architecture design, cloud service integration, and optimization.
Background
Traditional vehicle verification processes at security checkpoints are often slow and manual, increasing the risk of errors and delays. This project sought to address these issues by utilizing AWS’s advanced cloud services to automate vehicle detection and enhance decision-making with AI-driven insights. The solution was designed to support scalability, accuracy, and real-time decision-making while ensuring data security and cost-efficiency.
This category details the step-by-step approach taken during the project, including research, planning, design, development, testing, and optimization phases.
Research & Planning
Mapped out system requirements, including the need for real-time image processing, secure data storage, and automated alert systems. Outlined key AWS components needed for the project: EC2, S3, Lambda, DynamoDB, SQS, Rekognition, and SNS.
Design & Prototyping
Prototyped the workflow using Python scripts and CloudFormation templates, ensuring scalability and fault tolerance.
Implementation
Image Capture and Upload: An EC2 instance simulated a security camera to capture vehicle images, uploading them automatically to an S3 bucket every 30 seconds via Python scripts. Workflow and Automation: Configured an SQS queue to link S3 with a Lambda function, which utilized AWS Rekognition to analyze images, extracting vehicle labels and license plate details. Data Storage and Alerts: DynamoDB tables stored vehicle information, identifying whether vehicles were whitelisted, blacklisted, or unknown, while Amazon SNS sent email notifications for flagged vehicles.
Testing & Optimization
Performed testing to validate system accuracy, including image processing, vehicle identification, and database updates. Optimized Lambda functions for performance and cost-efficiency by reducing execution time and batching SQS messages.
The resulting vehicle security control system leverages AWS’s advanced services to automate the verification process. Key features include:
Real-Time Vehicle Identification
AWS Rekognition analyzes images to detect vehicle types and license plates with high accuracy.
Secure Data Workflow
S3, SQS, and Lambda ensure smooth data flow from image capture to analysis and storage
Automated Alerts
Amazon SNS sends email notifications for blacklisted or unrecognized vehicles, enabling swift action
Accurate Detection
Increased Efficiency
Rekognition achieved a confidence level of over 90% in label and text detection, ensuring reliable identification
Cost Efficiency
Optimized resource usage reduced operational costs by leveraging AWS’s pay-as-you-go model
Scalability
The modular architecture enables easy scaling to accommodate larger deployments or additional checkpoints