Releases: open-edge-platform/dlstreamer
2024.2.0
Intel® Deep Learning Streamer Pipeline Framework Release 2024.2.0
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.
This release includes Intel® DL Streamer Pipeline Framework elements to enable video and audio analytics capabilities, (e.g., object detection, classification, instance segmentation, 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 |
| gvaattachroi | Provides an ability to define one or more regions of interest to perform inference on, instead of the full frame. |
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 |
|---|---|
| New models support: Yolov10 for GPU, DeepLabv3 | Support for most recent Yolov10 model for GPU and DeepLabv3 (semantic segmentation) |
| UTC format for timestamp | Timestamp can be shown in UTC format based on system time with option to synchronize it from NTP server |
| OpenVINO 2024.4 support | Update to latest version of OpenVINO |
| GStreamer 1.24.7 support | Update to latest version of GStreamer |
| Intel® NPU 1.6.0 driver support | Support for newer version of Intel® NPU Linux driver |
| Simplified installation process for option#1 (i.e. Ubuntu packages) via script | Development of the script that enhances user experience during installation of Intel® DL Streamer with usage of option#1. |
| Documentation improvements | Descriptions enhancements in various points. |
| [Preview feature] Simplified installation process for option#2 via script | Development of the script that enhances user experience during installation of Intel® DL Streamer with usage of option#2.. |
Fixed Issues
| Issue | Issue Description |
|---|---|
| Github issue: #431 | WARNING: erroneous pipeline: no element "gvadetect" |
| Github issue: #433 | WARNING: erroneous pipeline: no element "gvaattachroi" inside Docker image 2024.1.2-dev-ubuntu24 |
| Github issue: #434 | Proper way to use object-class under gvadetect |
| Github issue: #435 | No such element or plugin 'gvadetect' |
| Internal findings | installation via option#3 documentation fixes; fixed hangs on MTL NPU for INT8 models; fixed issues with using 4xFlex170 system |
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#1 via script | On some configurations there may be some errors visible |
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.
Legal Information
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, or from the OEM or retailer.
No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses.
You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel, the Intel logo, and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.
GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additi...
2024.1.2
Intel® Deep Learning Streamer Pipeline Framework Release 2024.1.2
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, instance segmentation, 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 |
| gvaattachroi | Provides an ability to define one or more regions of interest to perform inference on, instead of the full frame. |
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 |
|---|---|
| New models support: Yolov10 for CPU only,Yolov8 instance segmentation | Support for most recent Yolov10 model for CPU and extension for Yolov8 |
| New elements: gvaattachroi including documentation update + samples) | Added element documentation and sample development which introduces ability to define the area of interest on which the inference should be performed |
| OpenVINO 2024.3 support | Update to latest version of OpenVINO |
| GStreamer 1.24.6 support | Update to latest version of GStreamer |
| Ubuntu 24.04 support | Support for newer version of Ubuntu |
| Documentation updates for DeepStream to DL Streamer migration process | Updates to the migration process from Deep Stream |
| Documentation improvements | Descriptions enhancements in various points |
| [Preview feature] Simplified installation process for option#1 via script | Development of the script that enhances user experience during installation of DL Streamer with usage of option#1 |
Fixed Issues
| Issue | Issue Description |
|---|---|
| #425 | when using inference-region=roi-list vs full-frame in my classification pipeline, classification data does not get published |
| #432 | Installation issues with gst-ugly plugins |
| #397 | Installation Error DLStreamer - Both Debian Packages and Compile from Sources |
| Internal findings | custom efficientnetb0 fix, issue with selection region before inference, Geti classification model fix, dGPU vah264enc element not found error fix, sample: face_detection_and_classifiation fix |
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#1 via script | On some configurations there may be some errors visible |
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.
Legal Information
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, or from the OEM or retailer.
No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses.
You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel, the Intel logo, and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.
GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additional licenses. Intel is not responsible ...
2024.1.1
Intel® Deep Learning Streamer Pipeline Framework Release 2024.1.1
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 |
|---|---|
| Missing git package | Git package added to DLStreamer docker runtime image |
| VTune when running DLStreamer | Publish instructions to install and run VTune to analyze media + gpu when running DLStreamer |
| Update NPU drivers to version 1.5.0 | Update NPU driver version inside docker images |
| Instance_segmentation sample | Add new Mask-RCNN segmentation sample |
| Documentation updates | Enhance Performance Guide and Model Preparation section |
| Fix samples errors | Fixed errors on action_recognition, geti, yolo and ffmpeg (customer issue) samples |
Fix memory grow with meta_overlay |
Fix for Meta Overlay memory leak with DLS Arch 2.0 |
| Fix pipeline which failed to start with mobilenet-v2-1.0-224 model | |
| Fix batch-size error -> with yolov8 model and other yolo models |
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 |
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.
Legal Information
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, or from the OEM or retailer.
No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses.
You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel, the Intel logo, and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.
GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of GStreamer.
*Other names and brands may be claimed as the property of others.
© 2024 Intel Corporation.
2024.1.0
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.
Legal Information
Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software, or service activation. Learn more at intel.com, or from the OEM or retailer.
No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses.
You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel, the Intel logo, and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.
GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of GStreamer.
*Other names and brands may be claimed as the property of others.
© 2024 Intel Corporation.
2024.0.2
Intel® Deep Learning Streamer Pipeline Framework Release 2024.0.2
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, discreate 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 |
|---|---|
| Support for ‘gst-va’ in addition to ‘gst-vaapi’ | Support for ‘gst-va’ in addition to ‘gst-vaapi’ |
| Add support for EfficentNetv2 (classification), MaskRCNN (instance segmentation) and Yolo8-OBB (oriented bounding box) | New classification model supported EfficentNetv2, new instance segmentation model supported MaskRCNN and oriented bounding box model as well added Yolo8-OBB |
| Support additional GETI models: segmentation, obb | GETI public models support added |
| Generalized method to deploy new models without need for model-proc file | Support model information embedded into AI model descriptors according to OpenVINO Model API |
| Release docker images on DockerHUB: runtime | Added docker images on DockerHUB: runtime |
| Added support for OpenVINO 2024.1.0 | Added support for OpenVINO 2024.1.0 |
Acknowledgements
Thanks for contributions from the DL Streamer developer community:
@aminatef
@russkel
Fixed issues
| Issue # | Issue Description | Fix | Affected platforms |
|---|---|---|---|
| 407 | EfficientNet-B1 support | We do not plan to support older DL Streamer releases with API1.0, I highly recommend to switch to newer version compatible with latest OpenVINO | All |
| 410 | cant run againts my camera feed | Config error, user opened a new issue for tracking the yolo issue and was able to see cameras now | All |
| 412 | with Docker cmd, cant create and dowload models inside docker | Config error. Without $ sign, when assigning a value (using $ to retrieve the value of a variable, e.g. to print the value): `$ export MODELS_PATH=/home/dlstreamer/temp/models1 | All |
| 413 | ffmpeg_openvino build failed, LibAV | Possible config error, missing libraries but no feedback given for 2 weeks so the issue was closed | All |
| 415 | cant run against Efficientnet-b0 due to model exceeds allowable size of 10MB | Resolved, user was able to get it running with last suggestion to use implemented RealSense specific gstreamer plugins, like •https://github.com/WKDSMRT/realsense-gstreamer => realsensesrc •https://gitlab.com/aivero/legacy/public/gstreamer/gst-realsense => realsensesrc A couple years old... |
All |
| 416 | detection with yolo not available on latest | Continue with "merged" command-line, using videobox and or videomixer (or many different other ways from the internet). You might need to start again... and checking the setup on your HOST. I'm using Ubuntu 22.04LTS. Created a non-root-user. Adding the user to video and render groups. Installed docker and configured to use Docker as non-root (without using "sudo" when using "docker run"). Before starting the container, I just call xhost +. Passing the render-group-id to "docker run" (in my case --group-add=110) docker run -it --net=host --device=/dev/dri --device=/dev/video0 --device=/dev/video1 --group-add=110 -v ~/.Xauthority:/home/dlstreamer/.Xauthority -v /tmp/.X11-unix -e DISPLAY=$DISPLAY -v /dev/bus/usb dlstreamer /bin/bash (not using -u 0 --privileged) |
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.
Legal Information
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Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infring...
Contributors
Assets 29
2024.0.1
Intel® Deep Learning Streamer Pipeline Framework Release 2024.0.1
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, discreate 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 |
|---|---|
| Add support for latest Ultralytics YOLO models | Add support for latest Ultralytics YOLO models: -v7, -v8, -v9 |
| Add support for YOLOX models | Add support for YOLOX models |
| Support deployment of GETI-trained models | Support models trained by GETI v1.8: bounding-box detection and classification (single and multi-label) |
| Automatic pre-/post-processing based on model descriptor | Automatic pre-/post-processing based on model descriptor (model-proc file not required): yolov8, yolov9 and GETI |
| Docker image size reduction | Reduced docker image size generated from the published docker file |
Changed in this Release
Docker image replaced with Docker file
- Ubuntu 22.04 reduced docker file is released.
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 |
Fixed issues
| Issue # | Issue Description | Fix | Affected platforms |
|---|---|---|---|
| 390 | How to install packages with sudo inside the docker container intel/dlstreamer:latest | start the container as mentioned above with root-user (-u 0) docker run -it -u 0 --rm... and then are able to update binaries |
All |
| 392 | installation error dlstreamer with openvino 2023.2 | 2024.0 version supports API 2.0 so I highly recommend to check it and in case if this problem is still valid please raise new issue | All |
| 393 | Debian file location for DL streamer 2022.3 | Error no longer occurring for user | All |
| 394 | Custom YoloV5m Accuracy Drop in dlstreamer with model proc | Procedure to transform crowdhuman_yolov5m.pt model to the openvino version that can be used directly in DLstreamer with Yolo_v7 converter (no layer cutting required) * git clone https://github.com/ultralytics/yolov5 * cd yolov5 * pip install -r requirements.txt openvino-dev * python export.py --weights crowdhuman_yolov5m.pt --include openvino |
All |
| 396 | Segfault when reuse same model with same model-instance-id. | 2024.0 version supports API 2.0 so I highly recommend to check it and in case if this problem is still valid please raise new issue | All |
| 404 | How to generate model proc file for yolov8? | Added as a feature in this release | All |
| 406 | yolox support | Added as a feature in this release | All |
| 409 | ERROR: from element /GstPipeline:pipeline0/GstGvaDetect:gvadetect0: base_inference plugin initialization failed | Suggested temporarily - to use a root-user when running the container image, like docker run -it -u 0 [... .add here your other parameters.. ...], to get more permissions |
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
- 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.
Legal Information
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© 2024 Intel Corporation.
2024.0
Intel® Deep Learning Streamer Pipeline Framework Release 2024.0
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, discreate 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 |
|---|---|
| Intel® Core™ Ultra processors NPU support | Inference on NPU devices has been added, validated with Intel(R) Core(TM) Ultra 7 155H |
| Compatibility with OpenVINO™ Toolkit 2024.0 | Pipeline Framework has been updated to use the 2024.0.0 version of the OpenVINO™ Toolkit |
| Compatibility with GStreamer 1.22.9 | Pipeline Framework has been updated to use GStreamer framework version 1.22.9 |
| Updated to FFmpeg 6.1.1 | Updated FFmpeg from 5.1.3 to 6.1.1 |
| Performance optimizations | 8% geomean gain across tested scenarios, up to 50% performance gain in multi-stream scenarios |
Changed in this Release
Docker image replaced with Docker file
- Ubuntu 22.04 docker file is released instead of docker image.
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 |
|---|---|---|---|
| 397 | Installation Error DLStreamer - Both Debian Packages and Compile from Sources | Package dependencies have been updated. | All |
| 399 | Compilation err when building DLstreamer 2023-release with OpenVINO 2023.2.0 | DLStreamer no longer uses legacy OpenVINO™ APIs. | 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
- 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.
Legal Information
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© 2024 Intel Corporation.
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.
Legal Information
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© 2023 Intel Corporation.
Release 2022.3
Intel® Deep Learning Streamer Pipeline Framework Release 2022.3
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 |
|---|---|
| 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 |
| Full Ubuntu 22.04 Support | Intel® DL Streamer has moved primary support to the current Ubuntu 22.04 LTS release. Ubuntu 20.04 is still a supported OS but Docker Images and APT packages are based on 22.04 |
| Compatibility with OpenVINO™ Toolkit 2022.3 | Pipeline Framework has been updated to use the 2022.3.0 version of the OpenVINO™ Toolkit |
| Updated to FFmpeg 5.1 | Updated FFmpeg from 4.4 to 5.1 |
Changed in this Release
Deprecation Notices
- Ubuntu 20.04 is still supported but primary support has moved to the latest Ubuntu 22.04 LTS version
- See see full list of currently deprecated properties in this table
- YOLOv2 is no longer a supported model
Known Issues
| Issue | Issue Description |
|---|---|
| Object Tracking | If generate-objects is set to true as it can produce misaligned or extra object tracking bounding boxes |
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 |
| Exception Failed to construct OpenVINOImageInference | In case of error similar to this: basic_string_view::substr: __pos (which is 18446744073709551603) > __size (which is 47), please share versions of installed Debian packages in link above, or reinstall OS and follow install guide |
| Draw_face_attributes sample | This sample errors and reports inference request failed |
| Action recognition sample | Sample returns with no results. Changing object_classify to video_inference in samples/gstreamer/gst_launch/action_recognition/action_recognition.sh |
Fixed issues
| Issue # | Issue Description | Fix | Affected platforms |
|---|---|---|---|
| 325 | GStreamer benchmark sample gave "Permission denied" | Undefined variables (PROCESSES_COUNT and CHANNELS_PER_PROCESS) were used in benchmark_one_model.sh and benchmark_two_models.sh. | All |
System Requirements
Please refer to Intel® DL Streamer documentation.
Installation Notes
There are several installation options for Pipeline Framework:
- Install Pipeline Framework from APT repository
- 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.
Legal Information
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.
This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps.
The products and services described may contain defects or errors which may cause deviations from published specifications. Current characterized errata are available on request.
Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© 2023 Intel Corporation.
Release 2022.2
Intel® Deep Learning Streamer Pipeline Framework 2022.2
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, 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 YOLO v3-v5, 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 |
|---|---|
| Intel® Data Center GPU Flex Series Beta 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 |
| Updated to GStreamer 1.20.3 | Upgrades from GStreamer 1.18.4 to latest stable GStreamer 1.20.3. |
| YOLOv5 Support | Added YOLOv5 postprocessing support |
| Architecture 2.0 [Preview] | Includes memory interop header-only library for zero-copy buffer sharing on CPU and GPU, C++ elements, integration into GStreamer as three sub-components |
| New non-GStreamer samples 2.0 | Samples FFmpeg+OpenVINO and FFmpeg+DPCPP |
| New GStreamer samples 2.0 on bin-elements and sub-pipelines | face_detection_and_classification_cpu, action_recognition, instance_segmentation, roi_background_removal, classification_with_background_removal |
| New element object_track supporting object tracking on GPU | device=GPU in gvatrack device=GPU discontinued, instead use vaapipostproc ! object_track spatial-feature=sliced-histogram device=GPU ! vaapipostproc |
| New element sycl_meta_overlay supporting inference results visualization on GPU | device=GPU in gvawatermark device=GPU discontinued, instead use vaapipostproc ! opencv_meta_overlay attach-label-mask=true ! sycl_meta_overlay ! vaapipostproc |
New property labels-file in gvainference/gvadetect/gvaclassify elements |
Allows to pass labels as .txt file |
New property scale-method in gvainference/gvadetect/gvaclassify elements |
Allows to select scale method used in pre-processing |
| Compatibility with OpenVINO™ Toolkit 2022.2 | Pipeline Framework has been updated to use the 2022.2.0 version of the OpenVINO™ Toolkit |
Changed in this Release
Deprecation Notices
- Deprecated
device=GPUingvatrackandgvawatermark - Please see full list of currently deprecated properties in this table
Known Issues
| Issue | Issue Description |
|---|---|
| Artifacts on sycl_meta_overlay | Running inference results visualization on GPU via sycl_meta_overlay may produce some partially drawn bounding boxes and labels |
| Exception Failed to construct OpenVINOImageInference | In case of error similar to this: basic_string_view::substr: __pos (which is 18446744073709551603) > __size (which is 47), please share versions of installed Debian packages in link above, or reinstall OS and follow install guide |
Fixed issues
| Issue # | Issue Description | Workaround | Affected platforms |
|---|---|---|---|
| Backwards compatibility issues when using gvapython and GST 1.18. | Added support to preserve legacy compatibility with gvapython element. | All |
System Requirements
Please refer to Intel® DL Streamer documentation.
Installation Notes
There are several installation options for Pipeline Framework:
- 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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