v0.7.0-beta

Video Analytics Serving (VA 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 v0.7.0-beta

Title	Description
Standalone microservice available in docker hub	A docker image of the VA Serving REST service is available at intel/video-analytics-serving. The ready to use image contains the following reference pipelines and can also be used as the basis for derivative microservices. object_detection Detect and label objects object_classification Add meta-data to object detections such as subtype and color object_tracking As object_classification adding tracking identifier to metadata audio_detection Analyze audio streams for events such as breaking glass or barking dogs. action_recognition Classifies general purpose actions in input video such as tying a bow tie or shaking
Expanded source customization based on request (including transparent support for webcam)	Previously reference pipelines required changes to work with different types of camera sources. Now pipelines can be reused without modification with the proper source derived from the request thus supporting a wider range of cameras including webcams and 'GigE' industrial cameras.
Edge AI Extension	Added HTTP protocol support Improved gRPC performance by up to 2x over v0.6.1 by threaded input handling It has been moved to independent repository https://github.com/dlstreamer/edge-ai-extension
HDDL-R accelerator support for Ubuntu 20.04 container	HDDL-R requires additional dependencies no longer in the the OpenVINO base image – these have been added back.
OpenVINO 2021.4.2 support	Updated DL Streamer base image to openvino/ubuntu20_data_runtime:2021.4.2.
VA Client improvements	Numeric parameters are now supported. Results are now displayed from slow starting pipelines.

Issues Resolved by This Release

Description	Issue
RTSP re-streaming plays back at frame processing rate, not encoded rate.	#68
Docker build fails if no_proxy setting contains spaces	#88

Known Issues

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

Description	Issue
Docker build fails if directory name contains spaces	#38
Models can be picked up from previous build	#71
Difficult to get normalized coordinates for spatial analytics parameters	#87
Some public models from Open Model Zoo do not produce inference results	#89
REST API for status and stop ignores pipeline name and version	#92
EdgeX sample fails when run from behind a proxy	#97
Pipeline failure in some multi-GPU systems	#98

Tested Base Images

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

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