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

Using the PersonTracking API

Amazon Rekognition Video can track the paths of people in a video stored in an Amazon S3 bucket. You initiate this operation using the StartPersonTracking API, which requires the location of the S3 object and notification channel.

The following diagram illustrates the process for a production system.

Figure 5.7: The person-tracking process

Figure 5.7: The person-tracking process

  1. An actor uploads the video into an Amazon S3 bucket.
  2. An actor calls the StartPersonTracking API.
  3. Amazon Rekognition notifies an Amazon SNS topic of progress.
  4. The Amazon SNS topic forwards the notification:
    1. It invokes an AWS Lambda function to handle the callback.
    2. It copies the message for offline troubleshooting.
  5. An AWS Lambda function fetches the results using the GetPersonTracking API.

Uploading the video to Amazon S3

This chapter’s repository includes a couple of test videos under the tracking folder. Upload those files into the Amazon S3 bucket if...