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Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>

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Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
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# Intel® Deep Learning Streamer (Intel® DL Streamer) {#openvino_docs_dlstreamer}
Intel® DL Streamer is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines.
Intel® DL Streamer makes Media analytics easy:
* Write less code and get better performance
* Quickly develop, optimize, benchmark, and deploy video & audio analytics pipelines in the Cloud and at the Edge
* Analyze video and audio streams, create actionable results, capture results, and send them to the cloud
* Leverage the efficiency and computational power of Intel hardware platforms
Go to [Intel® DL Streamer documentation website](https://dlstreamer.github.io) for information on how to download, install, and use.
**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.
**Media analytics pipelines** transform media streams into insights through audio / video processing, inference, and analytics operations across multiple IP blocks.
\* Other names and brands may be claimed as the property of others.

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.. toctree::
:maxdepth: 1
:hidden:
:caption: Media Processing
:caption: Media Processing and Computer Vision Libraries
DL Streamer API Reference <https://openvinotoolkit.github.io/dlstreamer_gst/>
gst_samples_README
Intel® Deep Learning Streamer <openvino_docs_dlstreamer>
openvino_docs_gapi_gapi_intro
OpenVX Developer Guide <https://software.intel.com/en-us/openvino-ovx-guide>
OpenVX API Reference <https://khronos.org/openvx>
@ -102,5 +101,12 @@ The toolkit provides a [Performance Optimization Guide](optimization_guide/dldt_
## Graphical Web Interface for OpenVINO™ Toolkit
You can choose to use the [OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), a web-based tool that guides you through the process of converting, measuring, optimizing, and deploying models. This tool also serves as a low-effort introduction to the toolkit and provides a variety of useful interactive charts for understanding performance.
## Media Processing
The OpenVINO™ toolkit comes with several sets of libraries and tools that add capability and flexibility to the toolkit. These include [DL Streamer](@ref gst_samples_README), a utility that eases creation of pipelines via command line or API, and optimized versions of OpenCV and OpenCL.
## Media Processing and Computer Vision Libraries
The OpenVINO™ toolkit also works with the following media processing frameworks and libraries:
* [Intel® Deep Learning Streamer (Intel® DL Streamer)](@ref openvino_docs_dlstreamer) — A streaming media analytics framework based on GStreamer, for creating complex media analytics pipelines optimized for Intel hardware platforms. Go to the Intel® DL Streamer [documentation](https://dlstreamer.github.io/) website to learn more.
* [Intel® oneAPI Video Processing Library (oneVPL)](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/api-based-programming/intel-oneapi-video-processing-library-onevpl.html) — A programming interface for video decoding, encoding, and processing to build portable media pipelines on CPUs, GPUs, and other accelerators.
You can also add computer vision capabilities to your application using optimized versions of [OpenCV](https://opencv.org/) and [OpenVX](https://khronos.org/openvx).