Advanced Car Tracking & Number Plate Recognition System on the Road
Challenge
This project involved implementing a solution for detecting and tracking moving vehicles, with the subsequent extraction of number plate areas, known as automatic number plate recognition (ANPR). It combines computer vision and artificial intelligence to automatically detect, track, and analyze moving vehicles in real-time.
The system is designed to meet the modern transportation, urban mobility, and security needs, encompassing traffic monitoring, smart parking, border control, tolling, and surveillance.
Input: camera feed
Output: detected car, extracted plate number
Technologies & Skills:
- Artificial Intelligence
- Computer Vision
- Image Processing
- Object Detection & Tracking
- Optical Character Recognition (OCR)
- Text Detection
- Character Segmentation
- Neural Network
- Tools & Frameworks: Python, OpenCV, NumPy, Scikit-learn, TensorFlow, PyTorch
This project involved implementing a solution for detecting and tracking moving vehicles, with the subsequent extraction of number plate areas, known as automatic number plate recognition (ANPR). It combines computer vision and artificial intelligence to automatically detect, track, and analyze moving vehicles in real-time.
The system is designed to meet the modern transportation, urban mobility, and security needs, encompassing traffic monitoring, smart parking, border control, tolling, and surveillance.
Input: camera feed
Output: detected car, extracted plate number
Technologies & Skills:
- Artificial Intelligence
- Computer Vision
- Image Processing
- Object Detection & Tracking
- Optical Character Recognition (OCR)
- Text Detection
- Character Segmentation
- Neural Network
- Tools & Frameworks: Python, OpenCV, NumPy, Scikit-learn, TensorFlow, PyTorch
Solution
Project Development Approach
We designed the system to be robust capable of detecting and tracking vehicles in video streams, even under challenging conditions like motion blur, low lighting, and angled views. The system incorporates geometric correction, image optimization, and quality assessment to ensure optimal plate capture for subsequent processing.
Vehicle plates are localized through a hybrid method that combines the Stroke Width Transform (SWT) for text detection, blob analysis for identifying regions of interest, and contour extraction for precise boundary detection. This method ensures the system's ability to detect and localize vehicle plates accurately.
A neural network classifier, trained on Haar, statistical, and PCA (Principal Component Analysis) features, validates plate regions, with accuracy tuned using ROC (Receiver Operating Characteristic) evaluation.
Project Development Approach
We designed the system to be robust capable of detecting and tracking vehicles in video streams, even under challenging conditions like motion blur, low lighting, and angled views. The system incorporates geometric correction, image optimization, and quality assessment to ensure optimal plate capture for subsequent processing.
Vehicle plates are localized through a hybrid method that combines the Stroke Width Transform (SWT) for text detection, blob analysis for identifying regions of interest, and contour extraction for precise boundary detection. This method ensures the system's ability to detect and localize vehicle plates accurately.
A neural network classifier, trained on Haar, statistical, and PCA (Principal Component Analysis) features, validates plate regions, with accuracy tuned using ROC (Receiver Operating Characteristic) evaluation.
Results
Solution & Results
The solution processes video feeds from traffic cameras or mobile devices to provide real-time insights into vehicular movement patterns and individual vehicle identification. The final application is a sophisticated AI-powered vehicle monitoring system that combines object detection, tracking, and ANPR in one pipeline.
Highlights:
- Real-time vehicle detection and classification
- Multi-object tracking across video frames
- Automatic license plate extraction with quality optimization
This solution can be tailored for multiple use cases, including:
- Smart Cities: traffic monitoring, parking automation, urban mobility analytics
- Transportation & Logistics: fleet tracking, toll collection, cargo monitoring
- Security & Law Enforcement: stolen car detection, border control, surveillance
- Commercial Applications: access control in gated facilities, private parking systems
Solution & Results
The solution processes video feeds from traffic cameras or mobile devices to provide real-time insights into vehicular movement patterns and individual vehicle identification. The final application is a sophisticated AI-powered vehicle monitoring system that combines object detection, tracking, and ANPR in one pipeline.
Highlights:
- Real-time vehicle detection and classification
- Multi-object tracking across video frames
- Automatic license plate extraction with quality optimization
This solution can be tailored for multiple use cases, including:
- Smart Cities: traffic monitoring, parking automation, urban mobility analytics
- Transportation & Logistics: fleet tracking, toll collection, cargo monitoring
- Security & Law Enforcement: stolen car detection, border control, surveillance
- Commercial Applications: access control in gated facilities, private parking systems
