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
      • Websocket
      • Database Structure
    • 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
Powered by GitBook
On this page
  • Nodeflux Person Smoking Detection
  • Designed Applications
  • Architecture
  • Additional Necessary Deployment (OVOD)
  • Analytics Configuration

Was this helpful?

Export as PDF
  1. Visionaire Stream
  2. Analytics

Person Smoking Detection

PreviousPre-Flood MonitoringNextPerson with Handphone Detection

Last updated 9 months ago

Was this helpful?

Nodeflux Person Smoking Detection

Nodeflux Person Smoking Detection promptly identifies and addresses instances of smoking in restricted areas, promoting a safer and healthier environment. This solution integrates advanced Large Vision Models and Visual Transformers directly into the Nodeflux Visionaire platform. The analytics system is capable of automatically detecting individuals smoking in real-time scenarios.

Designed Applications

Nodeflux Person Smoking Detection is primarily intended for use in law enforcement and surveillance applications. It monitors the occurrence of smoking events and aids in mitigating or even preventing potential risks or threats associated with other unforeseen events that may transpire.

Disclaimer: In a sense of large vision models and visual transformers technology, the performance of this analytic might slightly differ in your environment, depending on several variables such as camera specs, camera height, camera angle, weather conditions, etc. We highly recommend you to test our analytics and run benchmarks on your own images, with ground truth or your quality expectations prepared beforehand. Please contact us for more info.

Architecture

Visionaire Person Smoking Detection utilizes a combination of other services:

  1. Postgres - For database,

  2. Docker Snapshot - For object detection related,

  3. Visionaire Docker Stream (must be v4.57.4 and above) - For video stream processing,

  4. Visionaire Dashboard (Optional) - Built-in dashboard for visualization.

Additional Necessary Deployment (OVOD)

Since Nodeflux Person Smoking Detection utilizes the snapshot platform, an additional docker-compose.yml file needs to be deployed alongside the other aforementioned services. The following directive link outlines the installation process for deploying the docker-compose.yml file specific to the Open Vocabulary Object Detection (OVOD) pipeline of snapshot platform:

Analytics Configuration

When you are assigning this analytics into the stream through the dashboard, you should configure some parameters setting below:

{
  "dump_interval": 3,      
  "address": "http://192.168.101.105:4006",
  "dump_confidence" : 0.3
}
Parameter
Explanation

dump_interval

How frequent you want the stream as a frame snapshot to get the inference result. Unit in second

address

The IP address and its port where the OVOD snapshot service is deployed

dump_confidence

The value of minimum confidence threshold so that the inference result should be dumped as the event. Unit ranged between 0.1 to 1

OVOD Installation
Person Smoking Detection Analytics