Release 2020.2

What’s New in This Release:

Title	High-level description
Gone Gold	GStreamer Video Analytics (GVA) is now Gold quality. Any API changes going forward will maintain backward compatibility.
Binary distribution via Intel Distribution of OpenVINO™ Toolkit	GVA binary version is now available for download via Intel Distribution of OpenVINO™ Toolkit as DL Streamer.
Simplified GVA Samples and improved documentation related to samples	GVA samples are simplified as follows: gst-launch samples: To demonstrate building pipeline with GVA elements via gst-launch command object tracking using gvatrack element pipeline customization and post-processing of inference using gvapython element C++ sample: to demonstrate how to build, control GStreamer pipeline from C++ application, and how to access metadata attached by inference elements to image buffer. Python sample: to demonstrate same capabilities as C++ sample Benchmark sample: to measure performance of single or multi-channel pipeline The updated documentation for all the samples is available here
Refinement of elements properties	To improve the developer experience some property names are modified and some are removed. Please see the below tables for details of changes.
Refinement of C++ and Python API	C++ and Python APIs are improved, see API reference for details
gvapython	Error handling is improved by reporting any failures or exceptions while loading the custom python modules. The element is enhanced to accept keword and positional arguments, to the custom modules, as JSON strings.
gvaidentify	This element has been converted to a sample that demonstrates how to match the face recognition embedding generated by gvaclassify against a set of known faces. The sample pipeline includes face detection, face recognition, and identification (matching).

Release details including known issues and system requirements are in available in the attached Release Notes.

Getting started is available on the Wiki.