What is the Intelligent Video Analytics ?

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Easy to use demo to demonstrate GPU accelerated Inference
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Based on the NGC Deepstream Container (https://ngc.nvidia.com/catalog/containers/nvidia:deepstream-l4t)
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Leverages Kubernetes, Helm, NGC, DeepStream
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Best deployed via Helm to deploy the application
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Does not require a Video Management System (VMS)
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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: (https://ngc.nvidia.com/catalog/containers/nvidia:deepstream-l4t)

Running the Video Analytics Demo

Prerequisites

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

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

1. helm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz --untar\n\n2. cd into the folder video-analytics-demo-l4t and update the file values.yaml\n\n3. Go to the ngcModel section to update the NGC Model as shown in below \n\n# Update the NGC Model used in Deepstream\nngcModel:\n  #NGC Model Pruned URL from NGC\n  getModel: ngc registry model download-version \"nvidia/tao/trafficcamnet:pruned_v1.0.1\"\n  #NGC model name\n  name: trafficcamnet\n  # Model File Name that will use in Deepstream\n  fileName: resnet18_trafficcamnet_pruned.etlt\n  # Model Config that needs to update\n  modelConfig: config_infer_primary_trafficcamnet.txt\n  #Do not update the Put Model\n  putModel: /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models/\n  \nngcConfig:\n  apikey: \"\"\n  ngcorg: \"nvidian\"\n  ngcteam: \"no-team\"\n

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

helm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz\n\nhelm install video-analytics-demo-l4t-0.1.2.tgz\n

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

1. helm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz --untar\n\n2. cd into the folder video-analytics-demo-l4t and update the file values.yaml\n\n3. 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\ncameras:\n camera1: rtsp://XXXX\n camera2: rtsp://XXXX\n\n4. helm install video-analytics-demo-l4t\n

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

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

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

helm install iva --values ./values.yaml video-analytics-demo-l4t\n

View the Video Output

Use the below WebUI URL to access deepstream application from browser

http://IPAddress of Node:31115\n

NOTE: WebUI application need at least 1.25GB storage on Jetson/ARM system

Download VLC Player from: https://www.videolan.org/vlc/ 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\n

IPAddress of the node can be viewed by executing ifconfig on the server node

License

The DeepStream SDK license is available within the container at the location opt/nvidia/deepstream/deepstream-6.0/LicenseAgreement.pdf. By pulling and using the DeepStream SDK (deepstream) container in NGC, you accept the terms and conditions of this license.

Technical Support

Email: EGXSupport@nvidia.com

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The container is based on the NVIDIA DeepStream L4T container","## What is the Intelligent Video Analytics ?\n\n- Easy to use demo to demonstrate GPU accelerated Inference\n\n- Based on the NGC Deepstream Container (https://ngc.nvidia.com/catalog/containers/nvidia:deepstream-l4t)\n\n- Leverages Kubernetes, Helm, NGC, DeepStream\n\n- Best deployed via Helm to deploy the application\n\n- Does not require a Video Management System (VMS)\n\n## What is DeepStream?\n\nDeepStream 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.\n\nMore information on the DeepStream container here: (https://ngc.nvidia.com/catalog/containers/nvidia:deepstream-l4t)\n\n### Running the Video Analytics Demo\n\n#### Prerequisites\n- Kubernetes Cluster\n- Helm/Tiller\n- NVIDIA GPU Operator\n- Run the helm chart\n\nIf you want to use the NGC Models in Video Analytics Demo application, follow the below steps\n\n```\n1. helm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz --untar\n\n2. cd into the folder video-analytics-demo-l4t and update the file values.yaml\n\n3. Go to the ngcModel section to update the NGC Model as shown in below \n\n# Update the NGC Model used in Deepstream\nngcModel:\n #NGC Model Pruned URL from NGC\n getModel: ngc registry model download-version \"nvidia/tao/trafficcamnet:pruned_v1.0.1\"\n #NGC model name\n name: trafficcamnet\n # Model File Name that will use in Deepstream\n fileName: resnet18_trafficcamnet_pruned.etlt\n # Model Config that needs to update\n modelConfig: config_infer_primary_trafficcamnet.txt\n #Do not update the Put Model\n putModel: /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models/\n \nngcConfig:\n apikey: \"\"\n ngcorg: \"nvidian\"\n ngcteam: \"no-team\"\n```\n\nExecute the below commands to deploy the Intelligent Video Analytics Demo with built-in Video and WebUI\n\n```\nhelm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz\n\nhelm install video-analytics-demo-l4t-0.1.2.tgz\n```\n\nIf you want to use camera as input, Please follow the below steps to deploy Intelligent Video Analytics Demo.\n\n```\n1. helm fetch https://helm.ngc.nvidia.com/nvidia/charts/video-analytics-demo-l4t-0.1.2.tgz --untar\n\n2. cd into the folder video-analytics-demo-l4t and update the file values.yaml\n\n3. 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\ncameras:\n camera1: rtsp://XXXX\n camera2: rtsp://XXXX\n\n4. helm install video-analytics-demo-l4t\n```\n\nTo use the rtsp steam output, add the below configuration when installing the helm chart\n\n```\ncat <
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