The Intelligent Traffic Signal Control System (SMARTFLOW) aims to optimize urban traffic flow using AI-based real-time traffic density analysis. The system dynamically adjusts signal timings based on live vehicle counts and density, ensuring smoother traffic management and reduced congestion at intersections.
🔍 Real-Time Object Detection
Uses YOLOv8 to detect vehicles like cars, buses, trucks, and motorcycles in each frame.
🔄 Robust Object Tracking
Employs BYTETracker to maintain consistent vehicle identities across frames, ensuring smooth and reliable tracking.
📏 Virtual Line Monitoring
Implements a configurable virtual line to count vehicles and analyze traffic patterns as they cross a defined boundary.
✏ Dynamic Annotations
Annotates video streams with bounding boxes, labels, and trace lines to visualize vehicle trajectories and crossing events.
🎥 Flexible Video Input
Supports both live webcam feeds and recorded video files, making it adaptable to various deployment scenarios.
📡 Hardware Integration for IoT-based Smart Traffic Control
ESP32 with RFID Scanner: Detects RFID tags on authorized vehicles (e.g., emergency vehicles, buses) for priority access.
| Component | Technology |
|---|---|
| Frontend | React |
| ML Model | YOLOv8 |
| RFID Code | CPP |
Clone the SMARTFLOW repository to your local machine:
git clone https://github.com/YourOrg/SMARTFLOW.git
cd SMARTFLOW && pip install -r requirements.txt- Live Video Input → Captured from a camera at an intersection.
- Vehicle Detection & Counting → YOLOv8 detects cars, bikes, and buses.
- Traffic Density Estimation →
area_counter.pycalculates the percentage. - Signal Adjustment → The backend dynamically modifies timings.
- Data Logging & Analytics → Historical trends stored in Firestore.
- 🚀 Reinforcement Learning (RL) for better traffic predictions.
- 🌍 Edge Computing for real-time processing on IoT devices.
- 📊 Historical Data Insights to improve urban traffic planning.
For inquiries, reach out to [email protected] or visit our GitHub.