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2025.0.0
Intel® Deep Learning Streamer Pipeline Framework Release 2025.0.0
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-v11, 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. |
| gvatrack | Performs object tracking using zero-term, or imageless tracking algorithms. Assigns unique object IDs to the tracked objects. |
| gvaaudiodetect | Performs audio event detection using AclNet model. |
| gvaattachroi | Adds user-defined regions of interest to perform inference on, instead of full frame. |
| gvafpscounter | Measures frames per second across multiple streams in a single process. |
| 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. |
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 |
|---|---|
| Enhanced support of Intel® Core™ Ultra Processors (Series 2) (formerly codenamed Lunar Lake); enabled va-surface-sharing pre-process backend. | Validated with Ubuntu 24.04, 6.12.3-061203-generic and the latest Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver v24.52.32224.5 |
| [preview] Enabled Intel® Arc™ B-Series Graphics [products formerly Battlemage] | Validated with Ubuntu 24.04, 6.12.3-061203-generic and the latest Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver v24.52.32224.5 + the latest public Intel Graphics Media Driver version + pre-rerelease Intel® Graphics Memory Management Library version |
| OpenVINO 2024.6 support | Update to the latest version of OpenVINO |
| Updated NPU driver | Updated NPU driver to 1.10.1 version. |
| Bug fixing | Running multiple gstreamer pipeline objects in the same process on dGPU leads to error; DLStreamer docker image build is failing (2024.2.2 and 2024.3.0 versions); Fixed installation scripts: minor fixes of GPU, NPU installation section; Updated documentation: cleanup, added missed parts, added DLS system requirements |
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 |
| Sporadic hang on vehicle_pedestrian_tracking_20_cpu sample | Using Tiger Lake CPU to run this sample may lead to sporadic hang at 99.9% of video processing, rerun the sample as W/A or use GPU instead |
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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