Traffic congestion and road safety are critical challenges in urban areas. To address these issues, we propose TrafficEye, an AI-powered traffic surveillance and analysis system. Utilizing computer vision and deep learning, TrafficEye analyzes live CCTV footage for vehicle detection, classification, and counting. The system also identifies license plates and detects anomalies such as accidents or unauthorized activities. This research presents the methodology, experimental results, and potential applications of TrafficEye in enhancing urban traffic management and public safety. The system's real-time notifications, supported by an integrated mobile application, aim to revolutionize traffic monitoring.
The rapid urbanization of cities has resulted in increased vehicular traffic, posing challenges to traffic management, public safety, and environmental sustainability. Traditional traffic surveillance systems lack the efficiency and accuracy to manage these challenges effectively. In this project, we propose TrafficEye, an AI-driven system that leverages computer vision techniques to provide real-time traffic analytics. The system is designed to automate vehicle detection, classification, and counting, along with license plate recognition and anomaly detection, contributing to improved urban traffic flow and public safety.
#problem Statement
Urban areas face significant challenges in traffic management due to manual surveillance, lack of real-time data, and delays in incident detection. An automated, intelligent traffic monitoring system is needed to ensure smoother traffic flow and enhance public safety.
The primary objective of this project is to develop a comprehensive AI-based traffic surveillance system capable of:
Detecting, classifying, and counting vehicles in real-time.
Recognizing license plates for tracking and regulation enforcement.
Identifying unusual activities, such as accidents or unauthorized behaviors.
Sending real-time notifications to stakeholders through a mobile application.
Several traffic monitoring systems exist, but they are often limited in scope and functionality. Previous studies have explored computer vision techniques for vehicle detection and classification using convolutional neural networks (CNNs). Additionally, license plate recognition has been implemented using optical character recognition (OCR). However, existing solutions rarely integrate all these capabilities into a single platform with real-time analytics and notification systems.
Our work builds on these studies by combining advanced AI techniques, such as YOLOv8 for object detection, and deep learning models for license plate recognition, into a unified system. Moreover, the inclusion of anomaly detection and a mobile app for real-time notifications makes TrafficEye a comprehensive traffic monitoring solution.
#Methodology
System Architecture
The system consists of the following components:
CCTV Integration: Captures live video footage from urban roads.
AI Models:
Vehicle Detection and Classification: YOLOv8 is used for detecting and categorizing vehicles (e.g., cars, buses, trucks, motorcycles).
License Plate Recognition: OCR-based model extracts alphanumeric details from detected plates.
Anomaly Detection: A pre-trained recurrent neural network (RNN) identifies unusual behaviors such as accidents or unauthorized parking.
Data Processing: Processes the video feed in real-time using OpenCV and TensorFlow.
Mobile Application: Developed using React Native for real-time alerts and analytics.
Implementation Details
Programming Languages: Python for AI models and data processing, React.js and Django for the mobile application backend.
Tools and Libraries: TensorFlow, OpenCV, YOLOv8, Google Firebase.
Dataset: Collected from publicly available traffic datasets and augmented with custom recordings from urban areas.
Hardware Requirements: NVIDIA GPU for training deep learning models, high-definition CCTV cameras.
The experimental results demonstrate that TrafficEye provides reliable and accurate traffic monitoring in real-time. The integration of various AI components ensures a holistic approach to traffic management. However, challenges such as poor lighting and occlusions in CCTV footage require further optimization. Additionally, real-world deployment would necessitate addressing scalability and computational efficiency.
Conclusion
TrafficEye demonstrates the potential of AI and computer vision in transforming urban traffic surveillance. By automating vehicle detection, license plate recognition, and anomaly detection, the system offers a comprehensive solution for urban traffic management and public safety. Future work will focus on optimizing the system for low-resource environments and integrating predictive analytics for traffic flow forecasting.
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