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qt_app_pyside1/docs/MQTT_INFLUXDB_GRAFANA_IMPLEMENTATION.md

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+# Hybrid Desktop + Services Architecture Implementation Plan
+
+## Overview
+This implementation adds MQTT + InfluxDB + Grafana to create a professional-grade monitoring system alongside the existing PySide6 desktop application.
+
+## Architecture
+```
+Desktop App (PySide6) → MQTT Broker → InfluxDB → Grafana Dashboard
+       ↓                    ↓              ↓            ↓
+Qt Signals         Real-time Events   Time Series    Rich Analytics
+Local UI           MQTT Topics        Database       Visualizations
+```
+
+## Phase 1: Prerequisites & Downloads
+
+### 1.1 Download Required Software (No Accounts Needed)
+
+#### Mosquitto MQTT Broker
+- **Download**: https://mosquitto.org/download/
+- **File**: mosquitto-2.0.18-install-windows-x64.exe
+- **Size**: ~8MB
+- **Purpose**: Message broker for real-time event streaming
+
+#### InfluxDB v2 
+- **Download**: https://portal.influxdata.com/downloads/
+- **File**: influxdb2-2.7.4-windows-amd64.zip
+- **Size**: ~90MB  
+- **Purpose**: Time series database for metrics storage
+
+#### Grafana
+- **Download**: https://grafana.com/grafana/download?platform=windows
+- **File**: grafana-10.2.2.windows-amd64.zip
+- **Size**: ~180MB
+- **Purpose**: Analytics dashboard and visualization
+
+### 1.2 Python Dependencies
+```bash
+pip install paho-mqtt influxdb-client grafana-api requests-async asyncio
+```
+
+## Phase 2: Service Installation
+
+### 2.1 Install Mosquitto MQTT
+1. Run mosquitto installer as Administrator
+2. Install to default location: `C:\Program Files\mosquitto\`
+3. Will create Windows service automatically
+4. Default port: 1883 (unencrypted), 8883 (encrypted)
+
+### 2.2 Setup InfluxDB
+1. Extract InfluxDB zip to `C:\InfluxDB\`
+2. Create data directory: `C:\InfluxDB\data\`
+3. Will run as standalone service
+4. Default port: 8086 (HTTP API)
+
+### 2.3 Setup Grafana  
+1. Extract Grafana zip to `C:\Grafana\`
+2. Create configuration directory
+3. Will run as standalone service
+4. Default port: 3000 (Web UI)
+
+## Phase 3: Configuration Files
+
+### 3.1 Mosquitto Configuration
+**File**: `C:\Program Files\mosquitto\mosquitto.conf`
+```conf
+# Smart Intersection MQTT Configuration
+port 1883
+listener 1883 0.0.0.0
+allow_anonymous true
+persistence true
+persistence_location C:\Program Files\mosquitto\data\
+log_dest file C:\Program Files\mosquitto\logs\mosquitto.log
+log_type all
+```
+
+### 3.2 InfluxDB Configuration  
+**File**: `C:\InfluxDB\config.yml`
+```yaml
+http-bind-address: ":8086"
+storage-engine: tsm1
+storage-directory: C:\InfluxDB\data
+storage-wal-directory: C:\InfluxDB\wal
+```
+
+### 3.3 Grafana Configuration
+**File**: `C:\Grafana\conf\defaults.ini` (modify)
+```ini
+[server]
+http_port = 3000
+domain = localhost
+
+[security]
+admin_user = admin
+admin_password = admin
+
+[database]
+type = sqlite3
+path = grafana.db
+```
+
+## Phase 4: Desktop Application Integration
+
+### 4.1 MQTT Publisher Service
+- Modify SmartIntersectionController to publish to MQTT topics
+- Topics: `traffic/detection`, `traffic/violations`, `traffic/performance`
+- Real-time event streaming
+
+### 4.2 InfluxDB Writer Service  
+- Background service to write metrics to InfluxDB
+- Time series data: FPS, object counts, processing times
+- Automatic batching and error handling
+
+### 4.3 Grafana Dashboard Integration
+- Pre-configured dashboards for traffic analytics
+- Real-time charts and graphs
+- Alerting system for violations
+
+## Phase 5: Service Management
+
+### 5.1 Windows Services Setup
+- Create batch scripts to start/stop services
+- Optional: Install as Windows services for auto-start
+- Health monitoring and restart capabilities
+
+### 5.2 Desktop Integration
+- Add service status monitoring to desktop app
+- Quick access buttons to open Grafana dashboards
+- Service health indicators in UI
+
+## Resource Usage Estimates
+- **Mosquitto**: ~5-10MB RAM
+- **InfluxDB**: ~50-100MB RAM  
+- **Grafana**: ~100-150MB RAM
+- **Total Additional**: ~200-300MB RAM
+
+## Benefits
+✅ Professional monitoring and analytics
+✅ Real-time event streaming via MQTT
+✅ Rich Grafana dashboards  
+✅ Multiple device/remote access capability
+✅ Time series data analysis
+✅ Automated alerting system
+✅ Scalable architecture
+
+## Implementation Timeline
+- **Phase 1-2**: 30 minutes (downloads + installation)
+- **Phase 3**: 15 minutes (configuration)  
+- **Phase 4**: 2 hours (desktop integration)
+- **Phase 5**: 30 minutes (service management)
+- **Total**: ~3-4 hours for complete implementation
+
+## Next Steps
+1. Download required software
+2. Follow installation guide
+3. Configure services
+4. Integrate with desktop application
+5. Create Grafana dashboards
+6. Test end-to-end functionality

qt_app_pyside1/docs/user-guide/how-to-use-smart-intersection.md

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+# How to Use Smart Intersection Features in Desktop App
+
+This guide explains how to use the Smart Intersection analytics features integrated into the Traffic Monitoring Desktop Application.
+
+## Overview
+
+The Smart Intersection features provide advanced scene-based analytics for traffic monitoring, including multi-camera object tracking and region-of-interest analysis.
+
+## Getting Started
+
+### 1. Launch the Application
+- Run `python main.py` from the qt_app_pyside1 directory
+- The application will start with Intel Arc GPU acceleration enabled
+- Wait for the splash screen to complete initialization
+
+### 2. Access Smart Intersection Features
+
+#### Configuration Panel
+1. Navigate to the **Config** tab in the main window
+2. Select **Smart Intersection** from the configuration categories
+3. Configure the following settings:
+   - **Camera Settings**: Add multiple camera sources for scene analytics
+   - **Tracker Parameters**: Adjust tracking sensitivity and frame rate handling
+   - **ROI Definition**: Define regions of interest for analytics
+   - **Performance Settings**: Optimize for your Intel Arc GPU
+
+#### Analytics Dashboard
+1. Go to the **Analytics** tab
+2. View real-time scene analytics including:
+   - Multi-camera object tracking
+   - Region-based event detection
+   - Traffic flow analysis
+   - Pedestrian safety monitoring
+
+#### VLM Insights
+1. The VLM insights panel provides AI-powered analysis
+2. Enable scene-based insights for enhanced understanding
+3. View contextual information about detected events
+
+## Key Features
+
+### Multi-Camera Scene Analytics
+- **Object Tracking**: Track objects across multiple camera views
+- **Scene Fusion**: Combine data from multiple cameras for comprehensive view
+- **Real-time Processing**: All processing occurs locally with GPU acceleration
+
+### Region of Interest (ROI) Analytics
+- **Crosswalk Monitoring**: Detect pedestrians in crosswalk areas
+- **Lane Analysis**: Monitor vehicle dwell time and traffic flow
+- **Safety Zones**: Define areas for safety monitoring and alerts
+
+### Performance Optimization
+- **Intel Arc GPU**: Optimized for Intel Arc GPU acceleration
+- **Local Processing**: No cloud dependency, all processing local
+- **Real-time Feedback**: Immediate response to traffic events
+
+## Configuration Options
+
+### Tracker Settings
+```json
+{
+    "max_unreliable_frames": 10,
+    "non_measurement_frames_dynamic": 8,
+    "non_measurement_frames_static": 16,
+    "baseline_frame_rate": 30
+}
+```
+
+### Camera Configuration
+- **Camera ID**: Unique identifier for each camera
+- **Position**: Physical position in the intersection
+- **Calibration**: Camera calibration parameters
+- **ROI Mapping**: Define which regions each camera monitors
+
+## Troubleshooting
+
+### Common Issues
+1. **GPU Not Detected**: Ensure Intel Arc GPU drivers are installed
+2. **Poor Tracking**: Adjust tracker parameters in configuration
+3. **Performance Issues**: Check GPU utilization in Performance tab
+
+### Performance Tips
+- Use appropriate frame rates for your hardware
+- Configure ROI regions efficiently
+- Monitor GPU memory usage
+- Adjust tracking parameters based on scene complexity
+
+## Advanced Usage
+
+### Custom ROI Definition
+1. Load video or connect cameras
+2. Pause on a representative frame
+3. Use the ROI drawing tools to define areas
+4. Save configuration for reuse
+
+### Integration with Existing Workflows
+- Export analytics data for further analysis
+- Configure alerts for specific events
+- Integrate with external systems via configuration
+
+## Support
+
+For additional help:
+- Check the **Help** → **User Guide** menu
+- Review system requirements
+- Consult troubleshooting documentation
+- Check performance metrics in the Performance tab

qt_app_pyside1/docs/user-guide/smart-intersection-overview.md

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+# Smart Intersection Analytics - Desktop Integration
+
+## Overview
+This documentation describes the Smart Intersection analytics capabilities integrated into the Traffic Monitoring Desktop Application. The system demonstrates how edge AI technologies can address traffic management challenges using scene-based analytics.
+
+## Key Features Integrated
+
+### Multi-Camera Scene Analytics
+- **Multi-camera multi-object tracking**: Enables tracking of objects across multiple camera views
+- **Scene based analytics**: Regions of interest that span multiple views can be easily defined
+- **Real-time processing**: Object tracks and analytics available in near real-time
+
+### Use Cases
+- **Pedestrian Safety**: Enhance safety for vulnerable road users (VRUs) at crosswalks
+  - Scene-based region of interest (ROI) analytics help identify VRUs actively using crosswalks
+  - Detect unsafe situations, such as pedestrians walking outside designated crosswalk areas
+- **Vehicle Analytics**: Measure average vehicle count and average dwell time in each lane
+  - Vehicles spending too much time in a lane indicates anomalies such as stalled vehicles, accidents, and congestion
+
+### Desktop Application Benefits
+- **Reduced TCO**: Works with existing cameras and simplifies business logic development
+- **Local Processing**: All analytics run locally with Intel Arc GPU acceleration
+- **Integrated UI**: Scene analytics configuration and monitoring within the main application
+- **Real-time Insights**: VLM-powered insights for enhanced understanding of traffic patterns
+
+## Configuration
+
+### Scene Analytics Settings
+The application includes configuration options for:
+- **Tracker Parameters**: Frame rate handling, measurement thresholds
+- **Camera Setup**: Multi-camera calibration and positioning
+- **ROI Definition**: Define regions of interest for analytics
+- **Performance Tuning**: Optimize for Intel Arc GPU acceleration
+
+### Access Through UI
+- Navigate to the **Config** tab in the main application
+- Select **Smart Intersection** settings
+- Configure parameters and apply changes
+- View live analytics in the **Analytics** tab
+
+## Technical Integration
+The desktop application integrates scene-based analytics through:
+- **Scene Adapter**: Python utilities for object detection processing
+- **Configuration Management**: JSON-based settings for tracker and scene parameters
+- **Signal Processing**: Qt signals for real-time data flow between components
+- **GPU Acceleration**: OpenVINO optimized pipelines for Intel Arc GPU
+
+## Getting Started
+1. Open the Traffic Monitoring Desktop Application
+2. Navigate to **Config** → **Smart Intersection**
+3. Configure your camera settings and ROI definitions
+4. Apply settings and view analytics in the **Analytics** tab
+5. Access detailed insights through the **Help** → **User Guide** menu