Intel® Deep Learning Streamer Pipeline Framework Release 2024.1.0
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, discrete GPU, integrated GPU and NPU.
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 |
|---|---|
| Switch to ‘gst-va’ as default processing path instead of ‘gst-vaapi’ | Switch to ‘gst-va’ as default processing path instead of ‘gst-vaapi’ |
| Add support for ‘gst-qsv’ plugins | Add support for ‘qsv’ plugins |
| New public ONNX models: Centerface and HSEmotion | New public ONNX models: Centerface and HSEmotion |
| Update Gstreamer version to the latest one (current 1.24) | Update Gstreamer version to the latest one (1.24.4) |
| Update OpenVINO version to latest one (2024.2.0) | Update OpenVINO version to latest one (2024.2.0) |
| Release docker images on DockerHUB: runtime and dev | Release docker images on DockerHUB: runtime and dev |
| Bugs fixing | Bug fixed: GPU not detected in Docker container Dlstreamer - MTL platform; Updated docker images with proper GPU and NPU packages; yolo5 model failed with batch-size >1; Remove excessive ‘mbind failed:...’ warning logs |
| Documentation updates | Added sample applications for Mask-RCNN instance segmentation. Added list of supported models from Open Model Zoo and public repos. Added scripts to generate DLStreamer-consumable models from public repos. Document usage of ModelAPI properties in OpenVINO IR (model.xml) instead of creating custom model_proc files. Updated installation instructions for docker images. |
Fixed issues
| Issue # | Issue Description | Fix | Affected platforms |
|---|---|---|---|
| 421 | Can we specify the IOU threshold in yolov8 post-process json like yolov5? | Same solution as in #394 | All |
| 420 | there is a customer's detect model need to support | Support for Centerface and HSEmotion added | All |
Known Issues
| Issue | Issue Description |
|---|---|
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 |
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
- Build Docker image from docker file 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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