FFmpeg video analytics release v0.3

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

New in This Release

In this release, it enables FFmpeg* analytics pipeline with the elementary inference features, including:

• Support OpenVINO^™ 2019 R2/R3 release.
• Support Intel® VCAC-A (Harker Height) accelerator card, which is one standard PCIe form factor
  accelerator card. It is equipped with Intel® Core^™ i3 CPU and Movidius^™ Myriad* X VPU.
• Enable the “throughput” performance mode, executing multiple infer requests asynchronously to
  improve the performance, on both CPU (for example, on XEON® E5) and VCAC-A (with 12 Myraid* X VPUs).
• Support cropping feature in “detect” filter, so user could specify part of the image for inference.
• Support HETERO plugin.
• Support Multi-Device plugin to run inference on multiple devices for higher throughput.
• New “metaconvert” filter to convert the inference results in AVFrame’s sidedata to pre-defined metadata
  format.
• The "detect" filter supports YOLO*v3 model for object detection.
• Support specifying configurations for different inference devices.
• Support asynchronous pre-processing in the inference filters.
• Support model reshape API (which is introduced in OpenVINO^™ 2019 R2)

Known Issues/limitations

This release is subject to the following limitations:

• Running the pipeline with Gen^™-accelerated hardware decoding and inference on Gen^™(GPU), there
  might be segfault issue happen (This issue doesn’t exist on OpenVINO^™ 2019 R1).
• Some models from Open Model Zoo (example, vehicle-license-plate-detection-barrier-0106.xml,
  license-plate-recognition-barrier-0001.xml) doesn’t support batch-size setting. This could be
  workarounded by setting batch-size as 1.
• When GPU-accelerated hardware decoding is enabled in the ffmpeg command line, there might be
  issue reported that hardware surface is not available. This could be workarounded by
  • setting appropriate “-extra_hw_frames” numbers and “nireq” numbers for each inference filter.
  • setting “-threads 1” to disable multiple-threading for decoding.
• OpenVINO^™ doesn't support C API up to 2019 R2 release, so we provided the C-API wrapper for the
  inference engine. This C API wrapper will be replaced when OpenVINO^™ support IE C API
  officially.
• The inference output supports limited number of pre-defined metadata format for use cases, including
  objects detection, emotion, age, gender, license plate, etc.

Release details are available in the attached release notes. Getting started is available on the Wiki or in the attached user guide.