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
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  • How VisionAIre Snapshot works?
  • As Enrichment Platform
  • As API Platform

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  1. Visionaire Snapshot

Overview

VisionAIre Snapshot is an advanced image processing module designed to seamlessly integrate with the VisionAIre security and surveillance ecosystem. As a specialized component of the VisionAIre Stream platform, VisionAIre Snapshot offers a sophisticated image analysis solution that complements real-time video stream processing with a deeper level of data extraction and enrichment.

With the ability to receive focused image data from the VisionAIre Stream, this powerful tool performs secondary AI-driven inferences to extract detailed attributes from detected objects or individuals. Whether it's identifying specific features of a person, such as clothing or demographic details, or analyzing objects for enhanced recognition, VisionAIre Snapshot enhances the accuracy and utility of surveillance data.

Unique Selling Points (USPs):

  1. Enhanced Detail Recognition:

    • VisionAIre Snapshot specializes in refining the detection process by providing a secondary, more focused analysis of visual data, extracting minute details that general surveillance may overlook.

  2. Seamless Integration:

    • Designed to work in tandem with the VisionAIre Stream, it offers a plug-and-play solution that requires minimal setup, ensuring that existing infrastructure can be upgraded without extensive downtime or resource investment.

  3. Real-time Data Enrichment:

    • Unlike traditional systems that may require manual data entry for enrichment, VisionAIre Snapshot automates this process in real-time, providing immediate, actionable insights.

  4. Scalable Analytics:

    • VisionAIre Snapshot is built to scale with your security needs. Whether you’re a small enterprise or a large-scale operation, it can handle increasing volumes of data without compromising performance.

  5. Customizable AI Models:

    • The module supports a variety of AI models that can be customized to meet the unique demands of different surveillance scenarios, ensuring that clients receive the most relevant data for their specific needs.

  6. Efficient Storage Management:

    • By focusing on critical data and discarding irrelevant background noise, VisionAIre Snapshot ensures that storage space is utilized effectively, reducing the costs associated with data management.

How VisionAIre Snapshot works?

As Enrichment Platform

In context VisionAIre Snapshot as enrichment, here's a focused explanation on that interaction:

Interaction Between VisionAIre Stream and VisionAIre Snapshot

  • Initial Processing: The VisionAIre Stream component is responsible for processing the live CCTV feed. It performs the first level of AI inference, such as detecting the presence of people within the video stream.

  • Cropping and Further Inference: Once the VisionAIre Stream detects a person, it crops the image around the detected individual and sends this cropped portion to the VisionAIre Snapshot. This action is likely designed to focus on a specific element within the frame for more detailed analysis.

  • Secondary Analysis: VisionAIre Snapshot receives the cropped image and performs a secondary level of inference. This could involve more detailed or specialized AI models that analyze attributes of the detected person, such as age, gender, clothing, or even actions.

  • Feedback Loop: After the VisionAIre Snapshot completes its analysis, it sends the enriched data back to the VisionAIre Stream. This data could be used to enhance the initial inference, contribute to the event data stored in the database, or trigger actions in the VisionAIre Dashboard, such as alerts or updates.

  • Resultant Actions: The enriched data from the VisionAIre Snapshot can lead to several possible outcomes, such as updating records in the database with detailed information, triggering real-time alerts on the VisionAIre Dashboard, or storing processed snapshots for later review.

This system enables a multi-tiered analysis approach, leveraging both the broad capabilities of VisionAIre Stream and the detailed processing power of VisionAIre Snapshot.

As API Platform

Below is an in-depth description of how each part of the system works together:

1. Users:

  • Initiation: The process begins with the users who interact with the system. Users could be system administrators, security personnel, or clients using the VisionAIre platform.

2. Fremis API:

  • Interaction Point: Users make requests or send API to the VisionAIre system through the Fremis API. This API likely serves as the interface for accessing various functionalities provided by VisionAIre Snapshot, such as managing data, or performing actions like sending POST image to the AI model.

3. VisionAIre Snapshot:

  • Core Processing Unit: This component processes the data according to the user's requests via the API. It might take live data from cameras or archived footage and extract relevant snapshots based on predefined criteria, such as the occurrence of a specific event or the detection of particular objects or individuals.

  • Data Handling: VisionAIre Snapshot could also be responsible for applying additional processing or analytics to the images, such as running them through AI models for object recognition, face detection, or other image analysis tasks.

4. Database:

  • Storage: The database stores the snapshots and possibly the results of any additional processing done by VisionAIre Snapshot. This database is essential for retaining historical data and for allowing users to query past events.

5. License:

  • Validation: Before any operation is carried out within the VisionAIre Snapshot, a license validation check may occur to ensure that the user's request complies with the terms of their license.

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Last updated 1 year ago

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