AWS Rekognition Graph Analysis – Person Label Accuracy

Last week I wrote a post evaluating AWS Rekognition accuracy in finding people in images. The analysis was performed using the Neo4j graph database.

As I noted in the original post – Rekognition is either very confident it has identified a person or not confident at all. This leads to an enormous number of false negatives. Today I looked at the distribution of confidence for the Person label over the last 48 hours.

You be the judge:

rekognition-person-label-confidence-distribution

Check out original post to see how the graph is created and constantly updated as images are created in the serverless IoT processing system.

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).

Continue reading “Analyzing AWS Rekognition Accuracy with Neo4j”

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.

Continue reading “Series – Part 3: Serverless Architecture – a practical implementation: Serverless REST API”

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.

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