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| 1 | +# Metro Vision AI App Recipe |
| 2 | + |
| 3 | +## Overview |
| 4 | + |
| 5 | +The Metro Vision AI App Recipe suite provides an integrated AI-powered solution for modern urban traffic management and monitoring. This comprehensive platform combines multiple transportation-focused applications built on a unified architecture, enabling cities and organizations to deploy intelligent transportation systems efficiently. |
| 6 | + |
| 7 | +**Available Applications:** |
| 8 | +- **Smart Intersection Management** (includes optional Scenescape components) - AI-driven traffic flow optimization and intersection monitoring |
| 9 | +- **Loitering Detection** - Real-time detection of loitering behavior in transportation hubs |
| 10 | +- **Smart Parking** - Automated parking space monitoring and management |
| 11 | +- **Smart Tolling** - Intelligent toll collection and vehicle classification |
| 12 | + |
| 13 | +### Key Features |
| 14 | + |
| 15 | +- **Real-time Video Analytics**: Process live video streams from traffic cameras with low latency |
| 16 | +- **Edge AI Processing**: Run inference locally to reduce bandwidth and improve response times |
| 17 | +- **Scalable Architecture**: Easily scale from single intersections to city-wide deployments |
| 18 | +- **Configurable Pipelines**: Customize detection parameters and object types for specific use cases |
| 19 | +- **MQTT Integration**: Seamless communication between components for event-driven responses |
| 20 | +- **Dashboard Monitoring**: Real-time visualization of traffic metrics and system performance |
| 21 | +- **Flexible Deployment**: Support for various camera types and video sources |
| 22 | +- **No-Code Configuration**: Set up complex rules and workflows through intuitive interfaces |
| 23 | + |
| 24 | + |
| 25 | +## How It Works |
| 26 | + |
| 27 | +The Metro Vision AI App Recipe architecture is designed to facilitate seamless integration and operation of various components involved in AI-driven video analytics for transportation and safety & security scenarios. The system processes real-time video feeds from traffic cameras, applies deep learning models for object detection and classification, and generates actionable insights for traffic management. |
| 28 | + |
| 29 | + |
| 30 | + |
| 31 | +### Core Workflow |
| 32 | + |
| 33 | +1. **Video Ingestion**: Live camera feeds or video files are ingested into the DL Streamer Pipeline Server |
| 34 | +2. **AI Processing**: Deep learning models perform real-time inference for vehicle detection, pedestrian tracking, and behavior analysis |
| 35 | +3. **Metadata Extraction**: Rich metadata including object coordinates, classifications, and tracking information is generated |
| 36 | +4. **Event Processing**: Node-RED processes the metadata and triggers appropriate responses based on configured rules |
| 37 | +5. **Data Distribution**: Results are distributed via MQTT to dashboards, alerts, and external systems |
| 38 | +6. **Visualization**: Real-time video streams with overlaid analytics are displayed through WebRTC viewers |
| 39 | + |
| 40 | +### Components |
| 41 | + |
| 42 | +- **DL Streamer Pipeline Server (VA Pipeline):** The core video analytics engine that processes video frames, performs AI inference using optimized deep learning models, and extracts rich metadata including vehicle classifications, pedestrian detection, and traffic flow metrics. |
| 43 | + |
| 44 | +- **Mosquitto MQTT Broker:** A lightweight message broker that facilitates real-time communication between all system components, enabling event-driven responses and seamless data flow throughout the smart city monitoring ecosystem. |
| 45 | + |
| 46 | +- **Node-RED:** A visual programming platform that allows users to create custom traffic management rules, automated responses to traffic events, and integration workflows without requiring extensive coding knowledge. |
| 47 | + |
| 48 | +- **WebRTC Stream Viewer:** Provides real-time visualization of processed video streams with AI-generated overlays, including bounding boxes around detected vehicles, pedestrians, and other objects of interest. |
| 49 | + |
| 50 | +- **Grafana Dashboard:** An advanced monitoring and analytics platform that displays real-time traffic metrics, system performance data, historical trends, and customizable alerts for operator. |
| 51 | + |
| 52 | +- **Inputs (Video Sources):** Supports various input types including IP cameras, RTSP streams, and video files, providing flexibility for different deployment scenarios and existing infrastructure integration. |
| 53 | + |
| 54 | +#### Optional Components |
| 55 | + |
| 56 | +- **Scenescape Management UI:** A web-based user interface for advanced scene configuration, camera calibration, and visual rule setup. Provides intuitive tools for defining detection zones, traffic lanes, and monitoring areas through a graphical interface. |
| 57 | + |
| 58 | +- **Scenescape Controller:** The backend service that manages scene configurations, processes spatial analytics, and coordinates between the Management UI and the video analytics pipeline. Handles complex scene understanding and geometric transformations. |
| 59 | + |
| 60 | +- **Scenescape Database (PostgreSQL):** A robust relational database that stores scene configurations, camera metadata, calibration parameters, and historical analytics data. Ensures data persistence and enables complex queries for reporting and analysis. |
| 61 | + |
| 62 | +- **InfluxDB:** A time-series database optimized for storing and querying high-frequency transportation metrics such as vehicle counts, traffic flow rates, speed measurements, and system performance data. Enables efficient historical analysis and trend monitoring. |
| 63 | + |
| 64 | +### Transportation-Specific Features |
| 65 | + |
| 66 | +- **Vehicle Detection & Classification:** Accurately identifies and classifies different vehicle types |
| 67 | +- **Pedestrian Tracking:** Real-time tracking of pedestrians afor safety monitoring |
| 68 | +- **Incident Detection:** Automatic detection of accidents, stopped vehicles, or unusual traffic patterns |
| 69 | +- **Zone-Based Monitoring:** Configurable regions of interest for specific area monitoring |
| 70 | +- **Multi-Lane Support:** Simultaneous monitoring of multiple traffic lanes and directions |
| 71 | + |
| 72 | +The DL Streamer Pipeline Server serves as the cornerstone of the solution, specifically optimized for transportation and traffic management scenarios. It leverages Intel-optimized deep learning models including pedestrian and vehicle detectors, traffic flow analyzers, and behavior recognition models to perform real-time inference on video streams. |
| 73 | + |
| 74 | +The system is designed with professionals in mind, offering pre-configured pipelines for common traffic monitoring scenarios while maintaining the flexibility to customize detection parameters, tracking algorithms, and alert thresholds. This approach enables rapid deployment of AI-powered traffic management solutions without requiring deep learning expertise from traffic engineers and city planners. |
| 75 | + |
| 76 | +Key advantages include edge-based processing that reduces latency for time-critical traffic decisions, support for multiple video sources to cover complex intersections, and integration capabilities with existing traffic management systems through standard protocols and APIs. |
| 77 | + |
| 78 | +**Advanced Capabilities with Optional Components:** |
| 79 | +When deployed with the optional Scenescape components, the system provides enhanced spatial analytics capabilities including precise vehicle trajectory tracking, accurate speed measurement, and advanced zone-based analytics. The InfluxDB integration enables long-term trend analysis, capacity planning, and performance optimization based on historical traffic patterns. |
| 80 | + |
| 81 | +## Use Cases |
| 82 | + |
| 83 | +### Smart Intersection Management |
| 84 | +- Real-time traffic light optimization based on vehicle and pedestrian counts |
| 85 | +- Emergency vehicle detection and priority signal control |
| 86 | +- Pedestrian safety monitoring at crosswalks |
| 87 | +- Traffic violation detection and automated enforcement |
| 88 | + |
| 89 | +### Loitering Detection |
| 90 | +- Security monitoring at transit stations and stops |
| 91 | +- Identification of suspicious behavior in hubs |
| 92 | +- Crowd management in high-traffic areas |
| 93 | +- Automated alerts for security personnel |
| 94 | + |
| 95 | +### Smart Parking |
| 96 | +- Real-time parking space availability tracking |
| 97 | +- Automated payment and access control |
| 98 | +- Parking violation detection |
| 99 | +- Optimization of parking space utilization |
| 100 | + |
| 101 | +### Smart Tolling |
| 102 | +- Automated vehicle classification for toll calculation |
| 103 | +- License plate recognition for electronic toll collection |
| 104 | +- Traffic flow optimization at toll plazas |
| 105 | +- Integration with existing tolling infrastructure |
| 106 | + |
| 107 | +## To Get Started |
| 108 | +- [Smart Intersection](smart-intersection/docs/user-guide/Overview.md) |
| 109 | +- [Smart Tolling](smart-intersection/docs/user-guide/Overview.md) |
| 110 | +- [Smart Parking](smart-parking/docs/user-guide/Overview.md) |
| 111 | +- [Loitering Detection](loitering-detection/docs/user-guide/Overview.md) |
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