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›Starting Microservices

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

Starting Tracker

Reads head detections from a JSON topic, and creates tracks of the detected persons.

Prerequisites

It is assumed that Multi-Graph Runner (MGR) is running, because object detection records are necessary input. For more information on running MGR, see Starting Multi-Graph Runner.

Required input topic:

base.cam.0.dets.ObjectDetectionRecord.json

Note: Not available for multiple input streams. This microservice can only process one input.

For information on Tracker configuration, see Configuring Tracker.

Starting the Tracker service

To start Tracker:

  1. Run the microservice:

    Attention! Before starting this microservice, the command below silently stops and removes the Docker container named uvap_kafka_tracker, if such already exists.

    $ "${UVAP_HOME}"/scripts/run_kafka_tracker.sh -- --net=uvap
    

    The output of the above command contains the following:

    • Information about pulling the required Docker image
    • The ID of the Docker container created
    • The name of the Docker container created: uvap_kafka_tracker

    There are more optional parameters for the run_kafka_tracker.sh script to override defaults. Use the --help parameter to get more details.

  2. Wait for approximately 30 seconds, then check if the containers are still running:

    $ docker container inspect --format '{{.State.Status}}' uvap_kafka_tracker
    

    Expected output:

    running
    
  3. Check the output:

    $ docker exec kafka kafka-console-consumer --bootstrap-server kafka:9092 \
      --topic base.cam.0.tracks.TrackChangeRecord.json
    

    Expected example output:

    {"end_of_track":false,"detection_key":"1563262804472_0","point":{"x":598,"y":150}}
    {"end_of_track":false,"detection_key":"1563262804472_2","point":{"x":804,"y":249}}
    {"end_of_track":true,"detection_key":""}
    % Reached end of topic base.tracks.TrackChangeRecord.json [0] at offset 4916
    {"end_of_track":false,"detection_key":"1563262804847_0","point":{"x":598,"y":150}}
    {"end_of_track":false,"detection_key":"1563262804847_2","point":{"x":804,"y":249}}
    {"end_of_track":false,"detection_key":"1563262804847_1","point":{"x":1077,"y":353}}
    

The following output topic is created:

base.cam.0.tracks.TrackChangeRecord.json
← Starting Multi-Graph RunnerStarting Pass Detector →
  • Prerequisites
  • Starting the Tracker service
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