Release 2019 R2

This release contains GStreamer* Video Analytics plugins that bring Deep Learning Inference capabilities to open-source framework GStreamer* and helps developers to build highly efficient and scalable video analytics applications. The solution:

• Extracts insights from video stream(s) using object detection, classification and recognition CNN models and sends video metadata to application or cloud service for further processing
• Leverages hardware acceleration for media and inference operations and heterogeneous execution across Intel CPU and GPU
• Provides flexible mechanism to quickly construct video analytics pipeline from highly optimized building blocks
• Deployable on both edge devices and cloud infrastructure, including deployment in Docker containers
• Can be used for various applications such as video surveillance and security, smart city, retail analytics, ad insertion and others

New features in this release:

• Support of OpenVINO™ Toolkit R2
• Scalable performance on Intel® Xeon® Scalable Processors and improved realtime stream density of video analytics workloads
• Optimal out-of-the-box throughput-oriented performance on supported systems
• Multi-device execution mode with Intel® Distribution of OpenVINO™ Toolkit

Optimization notice:

For optimal performance on Intel® Xeon® Scalable Processors, use "pre-proc=ie" option in inference elements. Please note that this is currently supported only with open source version of OpenVINO™ Toolkit.

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

Getting started is available on the Wiki