Learn to Build Real-Time Video AI Applications

Men walking through an office setting with hotspots to shows IVA.Learn the skills to transform raw video data from cameras into deep learning-based insights in real timeMen walking through an office setting with hotspots to shows IVA.

Video analytics rely on computerized processing and automatic analysis of video content to detect and determine temporal and spatial events. The field is anticipated to experience double-digit growth for the next decade, as videos are quickly becoming a primary media form for transferring information. 

As the amount of video data generated grows at unprecedented rates, so does the ability and desire to analyze this information. Intelligent video analytics (IVA), which uses computer vision to extract valuable information from unstructured video data, is at the cutting edge of this emerging field.

The computer vision revolution

Computer vision, which uses deep learning models to help machines understand visual data, has improved drastically over the past few years thanks to HPC and neural networks. It transforms pixels to usable data through a range of tasks such as image classification, object detection, and segmentation. 

Some of its use cases include behavior analysis, enhanced safety measures, operations management, optical inspection, and content filtering. It has also aided new industries such as autonomous vehicles, smart retail, smart cities, and smart healthcare. Recognizing the potential IVA holds, organizations are eager to develop applications that harness this technology.

Developing video AI applications

NVIDIA, through the DeepStream SDK and the TAO Toolkit, makes creating highly-performant video AI solutions easy and intuitive. The DeepStream SDK is a streaming analytics toolkit for constructing video processing pipelines. It provides the flexibility to select from various input formats, AI-based inference types, and outputs. Users also determine what to do with the results such as cold storage, composite for display, or further analysis downstream. 

On the other hand, the TAO Toolkit uses transfer learning to efficiently train vision models. The software was designed with an emphasis on acceleration and optimization for video AI applications known to be computationally intensive. It can be deployed on low-power IoT devices for real-time analytics.

A new course to you get started

To help get you started, the NVIDIA Deep Learning Institute is offering a self-paced course titled Building Real-Time Video AI Applications, which covers the entire process of developing IVA applications. 

This course provides an easy progression of foundational understanding, important concepts, terminologies, as well as a lab portion. The hands-on walk-through of the technical components provides an opportunity to build complete video AI applications. 

It’s complemented by thorough explanations in each step of the development cycle to help you confidently make implementation decisions for your own project. The course also highlights important performance considerations to optimize the video AI application and meet deployment requirements. 

Upon completion, you can earn a certificate of competency and begin to develop custom applications. Intelligent video analytics is an exciting area of AI with great opportunities.

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