NVIDIA DeepStream: New Features and Enhancements
Overview of the latest releases in NVIDIA DeepStream (6.2), highlighting new features such as the upgraded object tracking library and Graph Composer GUI.
💡 This article highlights the latest major release in DeepStream 6 and is intended to overview its new features briefly. In addition, we have previously published technical tutorials on how to use DeepStream and its Python bindings. We encourage you to explore our technical articles for more information on DeepStream.
NVIDIA introduced DeepStream SDK in May 2017 and has since been actively developing it with new features and capabilities. Over the past few years, the company has released multiple new versions of DeepStream, each with more advanced capabilities than the last. This rapid development has made DeepStream an increasingly powerful tool for developers and businesses alike.
| Version | Release Date |
|---|---|
| DeepStream 1.0 | May 2017 |
| DeepStream 2.0 | December 2017 |
| DeepStream 3.0 | September 2018 |
| DeepStream 4.0 | August 2019 |
| DeepStream 5.0 | August 2020 |
| DeepStream 6.0 | October 2021 |
| DeepStream 6.2 | January 2023 |
Since our previous series of articles on NVIDIA DeepStream in the past year (2022), DeepStream has undergone some exciting changes by adding new features and enhancements to this software development kit (SDK). There are two significant upgrades that stand out in DeepStream SDK’s recent development (6.0 version). One is the extensive library for object tracking, which allows for more precise tracking of objects in real-time. The other is the Graph Composer GUI application, which provides an intuitive and user-friendly graphic interface for creating AI application pipelines. These improvements make DeepStream even more powerful and accessible to developers who want to create cutting-edge AI applications. Now, it’s time to delve into the specifics.
Upgraded Object Tracking
Object tracking is a crucial aspect of computer vision and is gaining significance in various industries such as surveillance, retail, healthcare, and automotive, among others. With the latest DeepStream object tracking library, developers can easily integrate tracking into their applications, reducing both development time and costs. The library supports multiple tracking algorithms and provides robust tracking capabilities, such as appearance modeling, re-identification, and multi-object tracking. These features make it easier for developers to build sophisticated AI applications with efficient object tracking capabilities.
Introducing Graph Composer GUI
The Graph Composer GUI is another significant enhancement to the DeepStream SDK. This application enables developers to create complex AI application pipelines through a drag-and-drop interface without the need for advanced programming skills. This graphical interface significantly reduces application development’s complexity, thus reducing time to market. The Graph Composer GUI also offers an extensive library of pre-built components, making it easy to create complex pipelines for different use cases.
Overall, the new features and enhancements in the NVIDIA DeepStream SDK demonstrate the company’s commitment to making AI application development more accessible and efficient. The addition of the object tracking library and Graph Composer GUI make it easier for developers to build applications that leverage the power of AI and deep learning. With the continued growth of AI and computer vision, Nvidia DeepStream is well-positioned to help developers create innovative solutions for a wide range of industries.
At Galliot, we are committed to helping developers get the most out of the latest features in Nvidia DeepStream. We have been carefully reading and investigating the documentation and release notes to create comprehensive tutorials on these new features. So, stay tuned for our upcoming tutorials and learn how to use these powerful tools to create innovative AI applications.
💡 So far, we have published several articles in Galliot explaining various DeepStream capabilities and how to use them. You can find the following articles useful for further reading:
1) DeepStream Python Bindings; Customize your Applications
Introducing the most important DeepStream elements, plugins, and functions. Plus, talking about how to build a Pipeline and customize your video analytics applications.
2) NVIDIA DeepStream Example
Here we build a Face Anonymizer using DeepStream Python bindings as a real-world example of how you can use this tool to make your own applications.
3) Using DeepStream to deploy Galliot Adaptive Object Detection on Jetsons and X86s
Describing how we deployed Galliot’s Adaptive Learning object Detection Model on X86s and Jetson devices using NVIDIA DeepStream and Triton Inference Server.
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