Release Notes (v0.4.0-alpha-preview)

Video Analytics Serving is a python package and microservice for deploying hardware optimized media analytics pipelines. It supports pipelines defined in GStreamer* or FFMpeg* media frameworks and provides APIs to discover, start, stop, customize and monitor pipeline execution. Video Analytics Serving is based on Intel® Distribution of OpenVINO™ Toolkit - DL Streamer and FFmpeg Video Analytics.

New and Changed in Release v0.4.0-alpha-preview

This is a preview of an upcoming v0.4.0-alpha release that extends functionality as follows:

Title	Description
Programmatic input sources and destinations	Applications can now supply frames directly to pipelines and receive inference results. See the app_source_destination sample for more information.
Live Video Analytics AI extension	A sample lva_ai_extension with gRPC AI Extension that works with Microsoft* Live Video Analytics.
Model download tool	Introduction of Model Download Tool to fetch deep learning models that power your pipelines. Models are downloaded at docker build time so longer need to be checked in
Pipeline and model versioning	Pipeline and model versions are no longer restricted to integer values so can have more meaningful descriptions
Additional samples	EdgeX integration. Synchronized recorded video and inference result playback
Additional base images	Intel(R) distribution of OpenVINO(TM) Toolkit runtime container (v2020.4) is now a supported base image. Version 2021.1 will build and run but has not been validated.

Known Issues

Known issues can be found as GitHub issues. If you encounter defects in functionality, please submit an issue.

Description	Issue
Pre-built base images do not support audio detection	#31
Docker build fails if directory name contains spaces	#38

Tested Base Images

Supported base images are listed in the Building Video Analytics Serving document.

* Other names and brands may be claimed as the property of others.