GateWise: Enhancing Security and Convenience in Gated Communities Through License Plate Recognition
Course Instructor
Afsoon Yousefi Zowj
Abstract
Residential gated communities require efficient vehicle access management while ensuring robust security. Traditional methods often rely on manual processes or outdated technologies that struggle with accuracy, scalability, and adaptability to real-world conditions. To address these challenges, we propose GateWise, an automated vehicle management system leveraging Automated License Plate Recognition (ALPR) technology. GateWise integrates advanced object detection algorithms with a user-friendly web-based interface to streamline vehicle authorization, tracking, and security.
Existing ALPR systems face limitations due to environmental variability (e.g., poor lighting, weather conditions) and dataset constraints, leading to inaccuracies in license plate detection. Open-source models, while cost-effective, often lack diversity in training data, reducing their reliability in dynamic scenarios. These issues compromise both security and user experience, necessitating a more adaptive solution.
GateWise employs open-source deep learning frameworks, specifically YOLOv8, optimized for license plate detection. A centralized web interface enables administrators to add or remove license plates, view all registered plates, and manage permanent or guest access with customizable expiration dates and times, ensuring seamless control over community vehicle permissions.
The development process leverages pretrained YOLOv8 models for both car and license plate detection, enabling efficient and reliable object recognition without the need for custom model training. Detection is tested using a custom-collected dataset that reflects real-world gated community scenarios, including challenging conditions such as glare, rain, and low lighting. Once a license plate is detected, the system uses an OCR engine to extract the alphanumeric text. The extracted text is then refined through post-processing to ensure it complies with California license plate formatting standards, improving the consistency and accuracy of identification. The system is developed using Python-based frameworks, integrating OpenCV for image handling, Tesseract OCR for text extraction, and a lightweight database to manage license plate records.
GateWise is designed to scale across communities of various sizes, with a streamlined, web-based interface that allows administrators to manage vehicle access easily. By focusing on practical, real-world performance and incorporating validation tailored to local standards, GateWise delivers a dependable and adaptable solution for modern vehicle access control in residential communities.
This project demonstrates how AI-driven ALPR systems can balance security and usability. GateWise automates access control while offering administrators granular oversight through its intuitive platform. Future work may explore privacy enhancements and integration with broader smart community ecosystems.
GateWise: Enhancing Security and Convenience in Gated Communities Through License Plate Recognition
Residential gated communities require efficient vehicle access management while ensuring robust security. Traditional methods often rely on manual processes or outdated technologies that struggle with accuracy, scalability, and adaptability to real-world conditions. To address these challenges, we propose GateWise, an automated vehicle management system leveraging Automated License Plate Recognition (ALPR) technology. GateWise integrates advanced object detection algorithms with a user-friendly web-based interface to streamline vehicle authorization, tracking, and security.
Existing ALPR systems face limitations due to environmental variability (e.g., poor lighting, weather conditions) and dataset constraints, leading to inaccuracies in license plate detection. Open-source models, while cost-effective, often lack diversity in training data, reducing their reliability in dynamic scenarios. These issues compromise both security and user experience, necessitating a more adaptive solution.
GateWise employs open-source deep learning frameworks, specifically YOLOv8, optimized for license plate detection. A centralized web interface enables administrators to add or remove license plates, view all registered plates, and manage permanent or guest access with customizable expiration dates and times, ensuring seamless control over community vehicle permissions.
The development process leverages pretrained YOLOv8 models for both car and license plate detection, enabling efficient and reliable object recognition without the need for custom model training. Detection is tested using a custom-collected dataset that reflects real-world gated community scenarios, including challenging conditions such as glare, rain, and low lighting. Once a license plate is detected, the system uses an OCR engine to extract the alphanumeric text. The extracted text is then refined through post-processing to ensure it complies with California license plate formatting standards, improving the consistency and accuracy of identification. The system is developed using Python-based frameworks, integrating OpenCV for image handling, Tesseract OCR for text extraction, and a lightweight database to manage license plate records.
GateWise is designed to scale across communities of various sizes, with a streamlined, web-based interface that allows administrators to manage vehicle access easily. By focusing on practical, real-world performance and incorporating validation tailored to local standards, GateWise delivers a dependable and adaptable solution for modern vehicle access control in residential communities.
This project demonstrates how AI-driven ALPR systems can balance security and usability. GateWise automates access control while offering administrators granular oversight through its intuitive platform. Future work may explore privacy enhancements and integration with broader smart community ecosystems.
