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›Facial Properties and Recognition

Feature Demos

  • Starting Feature Demos
  • Person Detection

    • Head Detection Demo
    • Head Pose Demo
    • Human Skeleton Demo
    • Detection Filtering Demo

    Movement Detection

    • Tracking Demo
    • Pass Detection Demo

    Facial Properties and Recognition

    • Demography Demo
    • Face mask Demo
    • Single Camera Reidentification Demo with Pre-clustering
    • Reidentification Demo with Person Names

    Image and Video

    • Show Image Demo
    • Saving Video Streams

    Starting Microservices

    • Starting Multi-Graph Runner
    • Starting Tracker
    • Starting Pass Detector
    • Starting Reidentifier
    • Starting Detection Filter
    • Starting Feature Vector Clustering
    • Starting Stream Configurator UI
    • Starting Web Player
    • Starting Video Capture

Single Camera Reidentification Demo with Pre-clustering

This section demonstrates the setup and usage of the Reidentification feature in a one-camera system.

The Reidentifier microservice processes feature vectors and finds re-appearances. In the demo, identified people are marked with an orange bounding box and an auto increment ID. People not identified are marked with a grey bounding box. It is also possible to calculate the dwell time passed between the registration and reidentification.

Reidentification demo One-Camera System Registration

Reidentification demo One-Camera System Reidentification

In a one-camera system, a single camera registers and reidentifies the appearing people.

Reidentification uses single feature vetors of previously detected faces and compares them to newly detected ones to recognize reappearances. Pre-clustering creates clusters of recognized and stored feature vectors based on their similarity.

Each stored cluster has a representative feature vector which is continuously updated when recognizing new feature vectors that fit in an existing cluster (or a new cluster is created if the new feature vector does not match any existing clusters). Using pre- clustering can increase the accuracy of the reidentification by using representative feature vectors of the stored clusters instead of single ones.

Clustering block scheme Clustering Concept

Prerequisites

Before starting the demo, ensure the following:

  • UVAP is installed as instructed in Setting Up UVAP
  • UVAP is configured in fve demo mode as instructed in Configuring UVAP for FVE Demo Mode
  • The following microservices are running:
    • Multi-Graph Runner
    • Feature Vector Clustering
    • Reidentifier
  • Web display is started as instructed in Starting Web Player.

Reidentifier microservice requires video(s) with faces larger than 128 pixels, looking straight into the camera.

Required topics:

  • fve.cam.0.original.Image.jpg
  • fve.cam.0.dets.ObjectDetectionRecord.json
  • fve.cam.0.fvecs.FeatureVectorRecord.json
  • fve.cam.99.reids.ReidRecord.json

Starting One-Camera Reidentification Demo

Start the demo with run_demo.sh:

Attention! After the first run of these scripts, execute set_retention.sh script manually because new (*.Image.jpg) topics are created.

$ "${UVAP_HOME}"/scripts/run_demo.sh \
  --demo-name reidentification \
  --demo-mode fve -- --net uvap

Display in Web Browser

Navigate to the following URL to display the demo:

http://localhost:9999#fve.cam.0.reidentification.Image.jpg
← Face mask DemoReidentification Demo with Person Names →
  • Prerequisites
  • Starting One-Camera Reidentification Demo
  • Display in Web Browser
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