VisionAIre
  • About Nodeflux
  • Visionaire Stream
    • Overview
    • Requirements
      • Credential Keys
      • Tested Hardware
      • Bandwith Requirements
    • Installation Guide
      • Dependencies
      • Simple Installation
      • Advanced Installation
      • Clustering Installation
      • Add-ons Analytics Installation
        • People Attributes Installation
        • Vehicle Attributes Installation
        • OVOD Installation
        • OVIC Installation
        • VM Installation
        • VLM Installation
    • Analytics
      • Face Recognition
        • Additional Information
          • Overview
          • Disclaimer
          • Metrics
          • Testing
      • People Analytics
      • Crowd Estimation
      • PPE Detection
      • License Plate Recognition
      • Vehicle Analytics
      • Water Level Monitoring
        • Camera Guideline
      • Pre-Flood Monitoring
      • Person Smoking Detection
      • Person with Handphone Detection
      • Smoke and Fire Detection
      • Person Using Firearms Detection
      • Vandalism Attempt Recognition
      • ATM Burglary Incident Recognition
      • Road Crash Monitoring
      • People Fighting Recognition
      • Riot Recognition
    • Developer Guide
      • How our APIs work
      • HTTP APIs
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    • Changelogs
  • Visionaire Snapshot
    • Overview
    • Requirements
    • Installation Guide
      • Face Searching & Matching
        • Single Node
        • Clustering
      • Helmet Detection
      • Chicken Estimation
      • Face Detection
      • Over Dimension Over Load
      • Frontal License Plate Recognition
    • Analytics
      • Face Searching & Matching
        • Face Enrollment
          • Image Guideline
          • Face and Image Quality Assessment
            • Setup On Premise
            • API
          • Insert / Update / Delete Enrollment
          • Batch Enrollment
      • Helmet Detection
      • Chicken Estimation
      • People Demography
      • Face Detection
      • Over Dimension Over Load
      • License Plate Recognition -Frontal
    • Developer Guide
      • APIs
        • Face Searching & Matching
        • Helmet Detection
        • Chicken Estimation
        • People Demography
        • Face Detection
        • Over Dimension Over Load
        • Frontal License Plate Recognition
      • Vanilla APIs for Face Enrollment
      • Porting Enrollment Database Cluster to Docker
    • Changelogs
  • VisionAIre Dashboard
    • Introduction
    • Add Analytic Assignment
    • Accessing Vanilla Database
    • Connect to Vanilla Websocket
    • Create your own visualization
      • Migration from Old Streamer to New Streamer
      • Drawing Region of Interest
      • Additional Visualization Query
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  1. Visionaire Stream

Analytics

PreviousVLM InstallationNextFace Recognition

Last updated 1 year ago

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What are the Available Analytics in Docker Stream?

Analytics that are available in Docker Stream.

  • Recognize faces in the crowd.

  • Outstanding performance in various cases like hijab, ageing, surveillance angle, masked, and more.

  • Recognizing latency under 500ms.

  • Suitable for surveillance.

  • Count people crossing the line.

  • Recognize IN and OUT directions.

  • Track people within restricted or secure areas.

  • Analyze flow and density to optimize space usage and enhance safety.

  • Estimate the number of people in the crowd.

  • Suitable to monitor crowd conflict (protest, demonstration).

  • Monitor crowd density in real-time to prevent overcrowding and ensure public safety.

  • Monitor compliance with safety regulations by detecting the presence or absence of PPE.

  • The system provides real-time alerts and notifications for non-compliance, enhancing safety protocols.

  • Read vehicle plate and classify car

  • Support Indonesia plate.

  • Classify the vehicle to a car, motor, truck, or bus.

  • Detect unauthorized or suspicious vehicle activity in sensitive areas.

  • Classify the type of vehicle as a car, motor, truck, or bus

  • Counting vehicle crossing the line.

  • Monitor parking lot usage to improve availability and reduce congestion.

  • Detects the features and characteristics of water and monitors its movement inside a camera view.

  • Trends in water level changes over time.

  • Efficiency of early warning system in detecting flash floods.

  • Analysis of flood frequency and duration to understand changing climate impacts.

  • Real-time monitoring of smoking behavior in restricted areas.

  • Identification of patterns or trends in smoking activity.

  • Utilizes AI to detect whether individuals are using phones in specific areas.

  • Enhances security measures by identifying potential distractions or security risks.

  • Many more that can be explored

Face Recognition
People Analytics
Crowd Estimation
PPE Detection
License Plate Recognition
Vehicle Analytics
Water Level Monitoring
Pre-Flood Monitoring
Person Smoking Detection
Person with Handphone Detection
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