2024.2.2
Intel® Deep Learning Streamer Pipeline Framework Release 2024.2.2
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. |
| 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 |
|---|---|
| Installation of Intel® DL Streamer Pipeline Framework from Debian packages using APT repository | Support for apt-get install has been added. |
| Yolo11s-pose support | Added support for Yolo11s-pose model. |
| Change in gvafpscounter element | Reset FPS counters whenever a stream is added/removed. |
| OpenVINO updated | OpenVINO updated to the 2024.5 version. |
| GStreamer 1.24.9 | Updated GStreamer to the 1.24.9 version. |
| NPU 1.10.0 | NPU drivers updated to NPU 1.10.0 version. |
| Bugs fixing | Fixed issue with failing performance tests ; Fixed fuzzy tests ; Enabled debug mode ; Created TLS configuration that allows for secure communication between DL Streamer and MQTT broker; Fixed python error: init_threadstate: thread state already initialized; Fixed problem with DLS compilation / GSTreamer base plugin error.; Fixed issue with sample_test: python_draw_face_attributes on Ubuntu 24.04; Fixed issue with sample_test: gvapython cpu/gpu on Ubuntu 24.04 |
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 |
| Simplified installation process for option#2 via script | In certain configurations, users may encounter visible errors |
| Yolov8s model inference does not work on NPU when model converted with OpenVINO 2024.3 and 2024.4 | Model yolov8s converted with OpenVINO Python package in version 2024.3 and later cannot be used to inference on NPU. As a workaround for this model and similar situation, please change the version of installed ‘openvino’ package to 2024.2.0 in download_public_models.sh script (pip install openvino==2024.2.0) |
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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