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DeepStream - Intelligent Video Analytics Demo

Logo for DeepStream - Intelligent Video Analytics Demo
This is an easy to deploy video analytics demo that allows you to demo GPU accelerated video analytics. The container is based on the NVIDIA DeepStream container and leverages it's built-in SEnet with resnet18 backend.
Latest Version
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June 22, 2023

What is the Intelligent Video Analytics ?

  • Easy to use demo to demonstrate GPU accelerated Inference

  • Based on the NGC Deepstream Container

  • Leverages Kubernetes, Helm, NGC, DeepStream

  • Best deployed via Helm to deploy the application

  • Does not require a Video Management System (VMS)

What is DeepStream?

DeepStream SDK delivers a complete streaming analytics toolkit for real-time AI based video and image understanding and multi-sensor processing. DeepStream SDK features hardware-accelerated building blocks, called plugins that bring deep neural networks and other complex processing tasks into a stream processing pipeline. The SDK allows you to focus on building core deep learning networks and IP rather than designing end-to-end solutions from scratch.

More information on the DeepStream container here: (

Running the Video Analytics Demo


  • Kubernetes Cluster
  • Helm/Tiller
  • NVIDIA GPU Operator
  • Run the helm chart

If you want to use the NGC Models in the Video Analytics Demo application, follow the steps below.

1. helm fetch --untar

2. cd into the folder video-analytics-demo and update the file values.yaml

3. Go to the ngcModel section to update the NGC Model as shown in below.

# Update the NGC Model used in Deepstream
  #NGC Model Pruned URL from NGC
  getModel: ngc registry model download-version "nvidia/tao/trafficcamnet:pruned_v1.0.1"
  #NGC model name
  name: trafficcamnet
  # Model File Name that will use in Deepstream
  fileName: resnet18_trafficcamnet_pruned.etlt
  # Model Config that needs to update
  modelConfig: config_infer_primary_trafficcamnet.txt
  #Do not update the Put Model
  putModel: /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models/
  apikey: ""
  ngcorg: "nvidian"
  ngcteam: "no-team"

Execute the below commands to deploy the Intelligent Video Analytics Demo with built-in Video and WebUI

helm fetch

helm install video-analytics-demo-0.1.8.tgz

If you want to use camera as input, please follow the below steps to deploy Intelligent Video Analytics Demo.

1. helm fetch --untar

2. cd into the folder video-analytics-demo and update the file values.yaml

3. Go to the section Cameras in the values.yaml file and add the address of your IP camera. Read the comments section on how it can be added. Single or multiple cameras can be added as shown below
 camera1: rtsp://XXXX
 camera2: rtsp://XXXX

4. helm install video-analytics-demo

NOTE: For Video Analytics demo application you need 7GB of disk space on your system.

NOTE: For Video Analytics demo with A100 application you need 13GB of disk space on your system.

To use the rtsp steam output, add the below configuration when installing the helm chart

cat <<EOF | tee values.yaml
  type: NodePort
  port: 80
  rtspnodePort: 31113
  webuiPort: 5080
  webuinodePort: 31115

Run the below command to install the video analytics demo above custom values

helm install iva --values ./values.yaml video-analytics-demo

View the Video Output

  1. Use the below WebUI URL to access deepstream application from browser
    http://IPAddress of Node:31115/
  2. Download VLC Player from: on the client machine where the user intends to view the video stream.We can view the video stream by entering the following URL in the VLC player.
    rtsp://IPAddress of Node:31113/ds-test
    IPAddress of the node can be viewed by executing ifconfig on the server node


The DeepStream SDK license is available within the container at the location opt/nvidia/deepstream/deepstream-5.0/LicenseAgreement.pdf. By pulling and using the DeepStream SDK (deepstream) container in NGC, you accept the terms and conditions of this license.

Suggested Reading

For more information on DeepStream documentation containing Development guide, Plug-ins manual, API reference manual, migration guide, FAQ and release notes can be found here

If you have any questions or feedback, please refer to the discussions on DeepStream SDK Forum.

For more information, including blogs and webinars, see the DeepStream SDK website

Technical Support