Release 2023.0
Intel® Deep Learning Streamer Pipeline Framework Release 2023.0
Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Intel® Distribution of OpenVINO™ Toolkit Inference Engine backend, across Intel® architecture, CPU, and iGPU.
This release includes Intel® DL Streamer Pipeline Framework elements to enable video and audio analytics capabilities, (e.g., object detection, classification, audio event detection), and other elements to build end-to-end optimized pipeline in GStreamer* framework.
The complete solution leverages:
- Open source GStreamer* framework for pipeline management
- GStreamer* plugins for input and output such as media files and real-time streaming from camera or network
- Video decode and encode plugins, either CPU optimized plugins or GPU-accelerated plugins based on VAAPI
- Deep Learning models converted from training frameworks TensorFlow*, Caffe* etc. from Open Model Zoo (OMZ)
- The following elements in the Pipeline Framework repository:
| Element | Description |
|---|---|
| gvadetect | Performs object detection on a full-frame or region of interest (ROI) using object detection models such as YOLOv4, MobileNet SSD, Faster-RCNN etc. Outputs the ROI for detected objects. |
| gvaclassify | Performs object classification. Accepts the ROI as an input and outputs classification results with the ROI metadata. |
| gvainference | Runs deep learning inference on a full-frame or ROI using any model with an RGB or BGR input. |
| gvaaudiodetect | Performs audio event detection using AclNet model. |
| gvatrack | Performs object tracking using zero-term, or imageless tracking algorithms. Assigns unique object IDs to the tracked objects. |
| gvametaaggregate | Aggregates inference results from multiple pipeline branches |
| gvametaconvert | Converts the metadata structure to the JSON format. |
| gvametapublish | Publishes the JSON metadata to MQTT or Kafka message brokers or files. |
| gvapython | Provides a callback to execute user-defined Python functions on every frame. Can be used for metadata conversion, inference post-processing, and other tasks. |
| gvawatermark | Overlays the metadata on the video frame to visualize the inference results. |
| gvafpscounter | Measures frames per second across multiple streams in a single process |
For the details of supported platforms, please refer to System Requirements section.
For installing Pipeline Framework with the prebuilt binaries or Docker* or to build the binaries from the open source, please refer to Intel® DL Streamer Pipeline Framework installation guide
New in this Release
| Title | High-level description |
|---|---|
| Compatibility with OpenVINO™ Toolkit 2023.0 | Pipeline Framework has been updated to use the 2023.0.0 version of the OpenVINO™ Toolkit |
| Intel® Data Center GPU Flex Series PV support | Validated on Intel® Data Center GPU Flex Series 140 and 170 with pipelines/models/videos from the Intel® DL Streamer Pipeline Zoo, Pipeline Zoo Models and Pipeline Zoo Media repositories. Tested with the Latest GPU Linux release (https://dgpu-docs.intel.com/releases/production_682.14_20230804.html) |
| Updated to FFmpeg 5.1.3 | Updated FFmpeg from 5.1 to 5.1.3 |
| New media analytics model support | Added support for DeepSort and object tracking |
Changed in this Release
Deprecation Notices
- Ubuntu 20.04 is no longer actively supported.
- See see full list of currently deprecated properties in this table
- YOLOv2 is no longer a supported model
Known Issues
| Issue | Issue Description |
|---|---|
| Intermittent accuracy fails with YOLOv5m and YOLOv5s | Object detection pipelines using YOLOv5m and YOLOv5s show intermittent inconstancy between runs |
VAAPI memory with decodebin |
If you are using decodebin in conjunction with vaapi-surface-sharing preprocessing backend you should set caps filter using "video/x-raw(memory:VASurface)" after decodebin to avoid issues with pipeline initialization |
Artifacts on sycl_meta_overlay |
Running inference results visualization on GPU via sycl_meta_overlay may produce some partially drawn bounding boxes and labels |
| Preview Architecture 2.0 Samples | Preview Arch 2.0 samples have known issues with inference results |
Memory grow with meta_overlay |
Some combinations of meta_overlay and encoders can lead to memory grow |
Fixed issues
| Issue # | Issue Description | Fix | Affected platforms |
|---|---|---|---|
| 336 | Regarding the length and width of rectangular training yolov5, specify them separately in dlstreamer | Fixed layouts handling in YOLO post processing. | All |
System Requirements
Please refer to Intel® DL Streamer documentation.
Installation Notes
There are several installation options for Pipeline Framework:
- Install Pipeline Framework from pre-built Debian packages
- Pull and run Docker image
- Build Pipeline Framework from source code
For more detailed instructions please refer to Intel® DL Streamer Pipeline Framework installation guide.
Samples
The samples folder in Intel® DL Streamer Pipeline Framework repository contains command line, C++ and Python examples.
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