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@nszczygl9 nszczygl9 released this 19 Dec 11:29
· 35 commits to master since this release
v2025.2.0
539bce3

Deep Learning Streamer (DL Streamer) Pipeline Framework Release 2025.2.0

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.
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.

  • The following elements in the Pipeline Framework repository:

    Element Description
    gvaattachroi Adds user-defined regions of interest to perform inference on, instead of full frame.
    gvaaudiodetect Performs audio event detection using AclNet model.
    gvaaudiotranscribe Performs audio transcription using OpenVino GenAI Whisper model.
    gvaclassify Performs object classification. Accepts the ROI as an input and outputs classification results with the ROI metadata.
    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.
    gvafpscounter Measures frames per second across multiple streams in a single process.
    gvagenai Performs inference with Vision Language Models using OpenVINO™ GenAI, accepts video and text prompt as an input, and outputs text description. It can be used to generate text summarization from video.
    gvainference Runs deep learning inference on a full-frame or ROI using any model with an RGB or BGR input.
    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.
    gvamotiondetect Performs lightweight motion detection on NV12 video frames and emits motion regions of interest (ROIs) as analytics metadata.
    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.
    gvarealsense Provides integration with Intel RealSense cameras, enabling video and depth stream capture for use in GStreamer pipelines.
    gvatrack Performs object tracking using zero-term, or imageless tracking algorithms. Assigns unique object IDs to the tracked objects.
    gvawatermark Overlays the metadata on the video frame to visualize the inference results.

For the details on supported platforms, please refer to System Requirements.
For installing Pipeline Framework with the prebuilt binaries or Docker* or to build the binaries from the open source, refer to Intel® DL Streamer Pipeline Framework installation guide.

New in this Release

Title High-level description
Motion detection (gvamotiondetect) Performs lightweight motion detection on NV12 video frames and emits motion regions of interest (ROIs) as analytics metadata.
Audio transcription (gvaaudiotranscribe) Transcribes audio content with OpenVino GenAI Whisper model.
Gvagenai element added Performs inference with Vision Language Models using OpenVINO™ GenAI, accepts video and text prompt as an input, and outputs text description.
Models supported: MiniCPM-V, Gemma3, Phi-4-multimodal-instruct.
Deep SORT Preview version of Deep SORT tracking algorithm in gvatrack element.
gvawatermark element support on GPU Gvawatermark implementation extended about GPU support (CPU default).
Pipeline optimizer support 1st version of DL Streamer optimizer implementation added allowing end user finding the most FPS optimized pipeline.
GstAnalytics metadata support Enabled GstAnalytics metadata support.
OpenVINO custom operations Add support for OpenVINO custom operations.
D3D11 preprocessing enabled Windows support extended about D3D11 preprocessing implementation.
UX, Stability && Performance fixes • memory management fixes
• automatically select pre-process-backend=va-surface-sharing for GPU
• adjusting caps negotiations and preproc backend selection
• removing deleted element from all shared reference lists.
• using OpenCV preproc to convert sparse tensors to contiguous tensors
• creation of new VADisplay ctx per each inference instance
• remove need for dual va+opencv image pre-processing
Intel Core Ultra Panther Lake CPU/GPU support Readiness for supporting Intel Core Ultra Panther Lake CPU/GPU.
OpenVINO update Update to 2025.3 version.
GStreamer update Update to 1.26.6 version.
GPU drivers update Update to 25.40 version (for Ubuntu24)
NPU drivers update Update to 1.23 version.

Fixed Issues

# Issue Description
1 Fixed issue with segmentation fault and early exit for testing scenarios with mixed GPU/CPU device combinations.
2 Updated documentation for latency tracer.
3 Fixed issue where NPU inference required inefficient CPU color processing.
4 Fixed memory management for elements: gvawatermark, gvametaconvert, gvaclassify.
5 Improved model-proc check logic for va backend.
6 Fixed keypoints metadata processing issue for gvawatermark.
7 Fixed issue with missed gvarealsense element in dlstreamer image.
8 Fixed issue for scenario when vacompositor scale-method option didn't take affect.
9 Fixed documentation bug in the installation guide.
10 Fixed issue with same name for many python modules used by gvapython.
11 Fixed issue with draw_face_attributes sample (cpp) on TGL Ubuntu 24.
12 Fixed wrong pose estimation on ARL GPU with yolo11s-pose.
13 Fixed inconsistent timestamp for vehicle_pedestrian_tracking sample on ARL.
14 Fixed missing element 'qsvh264dec' in Ubuntu24 docker images.

Known Issues

Issue Issue Description
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.

Additional Information

System Requirements

Please refer to DL Streamer documentation.

Installation Notes

There are several installation options for Pipeline Framework:

  1. Install Pipeline Framework from pre-built Debian packages
  2. Build Docker image from docker file and run Docker image
  3. Build Pipeline Framework from source code

For more detailed instructions please refer to DL Streamer Pipeline Framework installation guide.

Samples

The samples folder in DL Streamer Pipeline Framework repository contains command line, C++ and Python examples.

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