Release Notes (v0.1.0-alpha)

Release Notes (v0.1.0-alpha)

Important note: This is a pre-production release of Video Analytics Serving. Be advised that there are defects in functionality beyond those listed below.

• This initial release contains Video Analytics Serving components and instructions to build and run examples within a docker container.
• Build Docker image and run the container with fully prepared environment
• This leverages GStreamer or FFmpeg to execute predefined pipelines.
• Current samples include pre-defined pipelines for:
  • GStreamer:
    • Object Detection
    • Emotion Recognition
  • FFmpeg:
    • Object Detection
    • Emotion Recognition
• The following models are included as samples in support of the above pipelines:
  • Object Detection
  • Face Detection
  • Face Reidentification
  • Landmarks Regression
  • Emotion Recognition

Known Issues

• Out of the box configuration currently limits one pipeline to run at a time. This will cause other pipelines to remain in QUEUED state until the running pipeline completes.
• Pipelines that run concurrently across multiple Video Analytics Serving containers and emit inferences to MQTT (through metapublish element) must use distinct/unique values for clientid.
• Pipelines with kafka as the method parameter for metapublish element will fail.

System Requirements

Hardware

• Refer to Intel OpenVINO SDK for inference plugin requirements.

Operating System

• Ubuntu 16.04/18.04
• Other operating systems that are supported via docker container (Not validated, experimental support).