NVIDIA DeepStream SDK
Improving operational efficiency and reducing loss are key issues facing many retailers. Today’s large supermarkets have numerous in-store cameras, which can be used to mitigate these problems, but real-time video processing of so many streams can be a challenge. By leveraging NVIDIA T4 GPUs, DeepStream and TensorRT, Malong’s state-of-the-art Intelligent Video Analytics (IVA) solution achieves 3X higher throughput with industry-leading accuracy to help their retail customers significantly improve their business performance.
Extracting actionable insights from a sea of data created by the world’s billions of cameras and sensors is a huge task, and maintaining a connection from these devices to the cloud for processing may be overly expensive or infeasible due to security, regulatory, or bandwidth restrictions. Microsoft Azure IoT Edge deploys applications and services built using DeepStream to edge devices, allowing organizations to process data locally to trigger alerts and take actions automatically and to upload to the cloud when needed. Combining Azure IoT Edge, NVIDIA DeepStream and Azure IoT Central brings device management, monitoring and custom business logic to millions of edge devices for real-time insights and easy deployment.
As a leader in fulfillment and logistics management, SF Technology needed to track goods and vehicles across tens of thousands of locations. Every site requires detailed analytics around fleet management, loading times, and other operational activities. Using DeepStream and NVIDIA GPUs, they were able to increase the efficiency of AI Argus; an intelligent video analytics product that brings smarter video insights and can process 32 video streams simultaneously. The company is also looking at using next-generation GPUs, which is expected to increase the number of video streams processed.
Start in Seconds, Scale Instantly with NGC
NGC is a container registry of GPU-optimized AI software. Applications built with the DeepStream SDK can be deployed on NVIDIA Tesla and Jetson platforms, enabling flexible system architectures and straightforward upgrades that greatly improve system manageability. The DeepStream SDK Docker containers with full reference applications are available on NGC.
End-to-End AI Workflow for IVA with Transfer Learning Toolkit
You can accelerate the development of efficient deep learning networks with the Transfer Learning Toolkit. The toolkit abstracts and accelerates deep learning training by allowing developers to fine-tune NVIDIA provided domain specific pre-trained models instead of going through the time-consuming process of building Deep Neural Networks (DNNs) from scratch. The pre-trained models accelerate the developer’s deep learning training process and eliminate higher costs associated with training models from scratch.
Transfer Learning Toolkit can also prune networks to tightly pack complex applications, delivering high throughput and stream density. When integrated with DeepStream, this offers an end-to-end deep learning solution for IVA.
Get Started With Hands-On Training
The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, engineers and researchers in AI and accelerated computing. Get experience with the DeepStream SDK in a self-paced course or request a full day workshop focused on deep learning for IVA by contacting DLI directly.
The DeepStream SDK is a general purpose streaming analytics SDK that enables system software engineers and developers to build high performance intelligent video analytics applications using NVIDIA Jetson or NVIDIA Tesla platforms.
The DeepStream SDK uses the open-source GStreamer framework to deliver high throughput with low latency. GStreamer is a library for constructing graphs of media-handling components. You can build applications ranging from simple video streaming and playback to complex processing using AI.
The DeepStream SDK can be used to build end-to-end AI-powered applications to analyze video and sensor data. Some popular use cases are: retail analytics, parking management, managing logistics, optical inspection and managing operations.
To learn about the DeepStream SDK versions supported for various NVIDIA products, click here.
Yes, the DeepStream SDK ships with 10+ reference samples. For more information about reference designs, see here.
DeepStream developer guide can be found here.
- Using custom YOLO models in DeepStream 4.0.1
- Migration to DeepStream 4.0.1
- Calibration of a 360 degree camera to yield geo-coordinates
- Building a Real-time Redaction App using NVIDIA DeepStream Part 1 | Part 2
- Build and Deploy Accurate Deep Learning Models for Intelligent Image and Video Analytics
- Breaking the Boundaries of Intelligent Video Analytics with DeepStream SDK
- Jetson Nano Brings AI Computing to Everyone
- Accelerating Intelligent Video Analytics using Transfer Learning Toolkit
- Multi-Camera Large-Scale Intelligent Video
- Accelerate Video Analytics Development with DeepStream SDK
Jetson community projects
- Check out Jetson community projects
- Post your questions or feedback in the DeepStream SDK forum.
- DeepStream edge-to-cloud with Azure IoT
- DeepStream SDK- Accelerating Real-Time AI Based Video And Image Analytics
- DeepStream: An SDK to Improve Video Analytics
- Real-time Object Detection for Disaster Response