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

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

{
    "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