Analyzing AWS Rekognition Accuracy with Neo4j

As an extension of my series of posts on handling IoT security camera images with a Serverless architecture I’ve extended the capability to integrate AWS Rekognition

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.

My goal is to identify images that have a person in them to limit the number of images someone has to browse when reviewing the security camera alarms (security cameras detect motion – so often you get images that are just wind motion in bushes, or headlights on a wall).

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Data is the currency of your Digital Transformation

This is a scary time for a company. But the state of play creates the potential for mass and creative disruption.
— $1 Billion for Dollar Shave Club: Why Every Company Should Worry @ NYTimes

Every company is a digital company. No longer is it a question of if your product will become digital – as was the case with music, newspapers, TV, movies, etc. – it is a question of how the experience of your product (and your company) changes even if your product isn’t digitized.

eCommerce, digital marketing, social, CRM, and content technology and strategies are critical. You will need to invest in those technologies – but underpinning all of those technologies is data – that data is the currency of your digital transformation.

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Series – Part 3: Serverless Architecture – a practical implementation: Serverless REST API

In part two of this series I discussed creating a serverless data collection and processing fabric for an IoT deployment. To recap, we’ve now reviewed the local devices and controller/gateway pattern for the security cameras deployed. We’ve also discussed the Amazon Web Services infrastructure deployed to collect, process and catalog the data generated by the security cameras.

In this post we will cover the creation of a serverless REST API.

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Series – Part 2: Serverless Architecture – a practical implementation: IoT Device data collection, processing and user interface.

In part one of this series I briefly discussed the purpose of the application to be built and reviewed the IoT local controller & gateway pattern I’ve deployed. To recap, I have a series of IP cameras deployed and configured to send (via FTP) images and videos to a central controller (RaspberryPI 3 Model B). The controller processes those files as they arrive and pushes them to Amazon S3. The code for the controller process can be found on GitHub.

In this post we will move on to the serverless processing of the videos when they arrive in S3.

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