Book Image

Computer Vision on AWS

By : Lauren Mullennex, Nate Bachmeier, Jay Rao
Book Image

Computer Vision on AWS

By: Lauren Mullennex, Nate Bachmeier, Jay Rao

Overview of this book

Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models. You’ll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that’ll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads. By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.
Table of Contents (21 chapters)
Part 1: Introduction to CV on AWS and Amazon Rekognition
Part 2: Applying CV to Real-World Use Cases
Part 3: CV at the edge
Part 4: Building CV Solutions with Amazon SageMaker
Part 5: Best Practices for Production-Ready CV Workloads

Automating a Video Analysis Pipeline

If a picture is worth a thousand words, what’s the value of a video? In the previous chapters, you’ve learned how to use Amazon Rekognition for image analysis. Now it’s time to introduce some techniques for handling video content.

You’ll collect frames from IP cameras and analyze them in the cloud. This task leverages the Real Time Streaming Protocol (RTSP), OpenCV, and Amazon Rekognition. Next, using the Amazon Rekognition Video’s Person Tracking API, you’ll extract walkways from video feeds. Furthermore, the demonstrated design patterns are serverless and elastically scale to virtually any size!

Figure 5.1: Target state architecture

Figure 5.1: Target state architecture

In this chapter, you’ll learn how to do the following:

  • Sample IP camera frames using OpenCV
  • Build an event-based analysis pipeline
  • Publish custom Amazon CloudWatch metrics
  • Track people’s paths within stored...