Hardware-Accelerated Video Analytics Training Course

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Course Code



14 hours (usually 2 days including breaks)


  • An understanding of deep neural networks
  • Python and C programming experience


  • Developers
  • Data scientists


Video analytics refers to the technology and techniques used to process a video stream. A common application would be capturing and identifying live video events through motion detection, facial recognition, crowd and vehicle counting, etc.

This instructor-led, live training (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.

By the end of this training, participants will be able to:

  • Install and configure the necessary development environment, software and libraries to begin developing.
  • Build, train, and deploy deep learning models to analyze live video feeds.
  • Identify, track, segment and predict different objects within video frames.
  • Optimize object detection and tracking models.
  • Deploy an intelligent video analytics (IVA) application.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline


Understanding Hardware Accelerated Decoding Methods

Overview of NVidia DeepStream SDK

Setting up the Development Environment

Preparing a Video Feed

Processing a Video Feed

Training a Deep Learning Model

How Transfer Learning Works

Improving the Model's Accuracy Through Transfer Learning

Developing a Neural Network Model to Track Moving Objects

Running a Video Analytics Inference Engine

Deploying the Inference Engine

Integrating a Deep Learning Model with an Application

Deploying an Intelligent Video Analytics (IVA) Application

Monitoring the Application

Optimizing the Inference Engine and Application


Summary and Conclusion



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