Dlstreamer
Deep Learning Streamer (DL Streamer) Pipeline Framework is an open-source streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines for the Cloud or at the Edge.
Install / Use
/learn @open-edge-platform/DlstreamerREADME
Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework
DL Streamer is now part of Open Edge Platform
Overview
<div align="center"><img src="intro.gif" width=900/></div>Deep Learning Streamer (DL Streamer) Pipeline Framework is an open-source streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines for the Cloud or at the Edge.
Media analytics is the analysis of audio & video streams to detect, classify, track, identify and count objects, events and people. The analyzed results can be used to take actions, coordinate events, identify patterns and gain insights across multiple domains: retail store and events facilities analytics, warehouse and parking management, industrial inspection, safety and regulatory compliance, security monitoring, and many other.
Backend libraries
DL Streamer Pipeline Framework is optimized for performance and functional interoperability between GStreamer* plugins built on various backend libraries
- Inference plugins use OpenVINO™ inference engine optimized for Intel CPU, GPU and VPU platforms
- Video decode and encode plugins utilize GPU-acceleration based on VA-API
- Image processing plugins based on OpenCV and DPC++
- Hundreds other GStreamer* plugins built on various open-source libraries for media input and output, muxing and demuxing, decode and encode
This page contains a list of elements provided in this repository.
Prerequisites
Please refer to System Requirements for details.
Installation
Please refer to Install Guide for installation options
To see the full list of installed components check the dockerfile content for Ubuntu24
Samples
Samples available for C/C++ and Python programming, and as gst-launch command lines and scripts.
Models
DL Streamer supports models in OpenVINO™ IR and ONNX* formats, including VLMs, object detection, object classification, human pose detection, sound classification, semantic segmentation, and other use cases on SSD, MobileNet, YOLO, Tiny YOLO, EfficientDet, ResNet, FasterRCNN, and other backbones.
See the full list of supported models, including models pre-trained with Intel® Geti™ Software, or explore over 70 pre-trained models in OpenVINO™ Open Model Zoo with corresponding model-proc files (pre- and post-processing specifications).
Other Useful Links
* Other names and brands may be claimed as the property of others.
License
The DL Streamer project is licensed under the MIT License license.
