A comprehensive AI-powered traffic management system that integrates real-time computer vision, traffic simulation, and emergency response capabilities. This intelligent system combines multiple detection models to create a unified traffic regulation platform for enhanced road safety and efficient traffic flow management.
Here is a complete demonstration of the system in action, showcasing vehicle detection, animal detection, and traffic regulation features.
🚀 Click the image above to watch the full demonstration video on YouTube
| Feature Category | Technologies | Capabilities |
|---|---|---|
| 🚗 Vehicle Detection | YOLOv8, OpenCV | Real-time traffic analysis, density monitoring, lane detection |
| 🦣 Animal Detection | YOLOv8, Computer Vision | Large animal detection on roadways, safety alerts |
| ⚡ Violence Detection | Sightengine API, AI Analysis | Real-time violence, gore, and weapon detection |
| 🛣️ Traffic Simulation | SUMO TraCI, Emergency Systems | Traffic light control, emergency routing, congestion management |
| 📱 Integration | SMS Alerts, Real-time Monitoring | Instant notifications, evidence logging, system automation |
| Model Type | Accuracy | Precision | Recall | F1-Score | Inference Speed |
|---|---|---|---|---|---|
| Vehicle Detection | 94.2% | 91.8% | 96.5% | 94.1% | 45 FPS |
| Animal Detection | 89.7% | 87.3% | 92.1% | 89.6% | 42 FPS |
| Violence Detection | 92.1% | 88.9% | 95.3% | 92.0% | Real-time API |
| Capability | Specification | Performance |
|---|---|---|
| Multi-Source Input | Webcam, IP Camera, RTSP, YouTube Live | Concurrent streams: 4+ |
| Real-Time Processing | Live analysis with minimal latency | < 50ms processing delay |
| Emergency Response | Automated alert system | < 3s notification time |
| Traffic Simulation | SUMO integration with TraCI | 1000+ vehicles/simulation |
| Evidence Logging | Automatic incident documentation | 99.9% data integrity |
_Traffic_Regulation/
├── 📁 YOLO Implementation/ # Main traffic analysis system
│ ├── 🤖 traffic_analysis.py # Core traffic detection engine
│ ├── 🛡️ violence_detector.py # Violence detection module
│ ├── 🐾 animal_detector.py # Animal detection system
│ ├── ⚙️ config.py # Configuration management
│ ├── 🧪 system_tools.py # Diagnostics and testing
│ └── 📊 models/ # Trained AI models
├── 📁 Animal_detection_grouped/ # Specialized animal detection
│ ├── 🦣 detection_code.py # Animal detection algorithm
│ ├── 📹 cow2.mp4 # Test video samples
│ └── 📈 animal_log.csv # Detection analytics
├── 📁 simulation_files/ # SUMO traffic simulation
│ ├── 🚦 updated_traffic_analysis.py # Emergency traffic control
│ ├── 🗺️ map.net.xml # Road network definition
│ ├── ⚙️ map.sumocfg # SUMO configuration
│ └── 🚗 routes.rou.xml # Vehicle routing patterns
├── 📁 evidence/ # Incident documentation
│ ├── 📸 animals/ # Animal detection evidence
│ ├── ⚠️ violence/ # Violence incident records
│ └── 🔫 weapons/ # Weapon detection logs
└── 📁 logs/ # System operation logs
├── 📝 violence_alerts.log # Security incident logs
└── 📊 violence_detection.log # Detection analytics
- Python 3.8+ with pip package manager
- CUDA-capable GPU (recommended for optimal performance)
- SUMO Traffic Simulator v1.23+ for simulation features
- Internet connection for API-based violence detection
- Clone the repository:
git clone https://github.com/prathamhanda/_Traffic_Regulation.git
cd _Traffic_Regulation- Set up Python environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate- Install dependencies:
cd "YOLO Implementation"
pip install -r requirements.txt- Configure the system:
python direct_setup.py # Interactive setup wizardcd "YOLO Implementation"
python traffic_analysis.py --source 0 # Webcam
python traffic_analysis.py --source video.mp4 # Video file
python traffic_analysis.py --source rtsp://ip:port/stream # IP cameracd Animal_detection_grouped
python detection_code.py # Process test videocd simulation_files
python updated_traffic_analysis.py # SUMO integrationLocation: YOLO Implementation/
Advanced traffic monitoring system with multi-source input support:
- Real-time vehicle detection using YOLOv8 neural networks
- Interactive lane calibration with point-and-click polygon creation
- Traffic density analysis with flow rate calculations
- Multi-source compatibility (webcam, video files, IP cameras, YouTube live streams)
- Headless mode operation for server deployment
Key Features:
- Automatic reconnection for live streams
- Configurable detection thresholds
- Real-time performance metrics
- Evidence preservation system
Location: Animal_detection_grouped/
Specialized detection system for large animals on roadways:
- Target species: Cows, horses, elephants, large wildlife
- Smart filtering: Excludes humans and vehicles
- Safety prioritization: Immediate alerts for road-blocking animals
- Analytics logging: Frame-by-frame detection records
Performance Metrics:
- Detection accuracy: 89.7%
- Processing speed: 42 FPS
- False positive rate: < 8%
Location: YOLO Implementation/violence_detector.py
AI-powered security monitoring using Sightengine API:
- Multi-threat detection: Violence, gore, weapons
- Real-time analysis: Asynchronous processing pipeline
- Configurable sensitivity: Adjustable detection thresholds
- Evidence preservation: Automatic incident documentation
- Alert system: Immediate notifications with severity levels
Location: simulation_files/
SUMO-based traffic simulation with emergency response:
- Dynamic traffic control: Adaptive signal timing
- Emergency vehicle prioritization: Route optimization
- Congestion management: Real-time flow adjustment
- Scenario modeling: Custom traffic patterns and events
SUMO Integration Features:
- TraCI real-time control
- Emergency routing algorithms
- Traffic light optimization
- Multi-lane intersection management
# View current configuration
python config.py --show
# Performance optimization preset
python config.py --preset performance
# Custom detection thresholds
python config.py --confidence 0.75 --violence-threshold 0.8Interactive calibration for custom road layouts:
- Start system with your video source
- Left-click to add polygon points
- Right-click to complete lane boundary
- Press 'C' to save configuration
Set up violence detection API:
python direct_setup.py # Interactive setup
# Enter your Sightengine API credentials when prompted- Live vehicle counts and traffic density
- Detection confidence scores
- System performance metrics
- Alert status and incident logs
- Automatic incident documentation
- Timestamped evidence preservation
- Structured logging for analysis
- Export capabilities for reporting
# System diagnostics
python system_tools.py --check
# Performance benchmarking
python system_tools.py --benchmark
# Stream connectivity testing
python system_tools.py --test-sources- SMS notifications for critical incidents
- Email alerts with evidence attachments
- Real-time dashboard updates
- API webhooks for external system integration
- Incident Detection → Immediate alert generation
- Evidence Capture → Automatic documentation
- Authority Notification → Multi-channel alerts
- Traffic Management → Dynamic signal adjustment
- Ensemble detection combining multiple AI models
- Cross-validation for improved accuracy
- Intelligent confidence scoring
- Adaptive threshold adjustment
- Continuous model improvement
- Transfer learning for custom scenarios
- Automated retraining capabilities
- Performance optimization algorithms
- Edge AI Deployment - Optimize for embedded systems
- 5G Integration - Ultra-low latency communication
- Blockchain Logging - Immutable incident records
- Predictive Analytics - AI-powered traffic forecasting
- Drone Integration - Aerial traffic monitoring
- Smart City Platform - Citywide deployment framework
- Ultralytics for the YOLOv8 framework
- Eclipse SUMO for traffic simulation capabilities
- Sightengine for violence detection API
- OpenCV community for computer vision tools
- Contributors who have helped improve this project
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