Analysis, Big Data, Data Architecture, IoT, Neo4j, Tech

Person Recognition in Images with OpenCV & Neo4j

Time for an update on my ongoing person identification in images project; for all the background you can check out these previous posts:

Analyzing AWS Rekognition Accuracy with Neo4j

AWS Rekognition Graph Analysis – Person Label Accuracy

Person Recognition: OpenCV vs. AWS Rekognition

In my earlier serverless series I discussed and provided code for getting images into S3 and processed by AWS Rekognition – including storing the Rekognition label data in DynamoDB.

This post builds on all of those concepts.

In short – I’ve been collecting comparative data on person recognition using AWS Rekognition and OpenCV and storing that data in Neo4j for analysis.

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Analysis, Big Data, Experimentation, IoT, Neo4j, Tech

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:

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

Analysis, Big Data, Data Architecture, IoT, Neo4j, Tech

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 Architecture, Emerging Technology Practice, IoT, Tech

Series – Part 5: Serverless Architecture – a practical implementation: Serverless web application.

In part four of the series I discussed securing the serverless REST API serving the collected IoT data from the security camera devices.

In this post I will cover deploying the web application that uses the REST API.

Continue reading “Series – Part 5: Serverless Architecture – a practical implementation: Serverless web application.” →

Data Architecture, Emerging Technology Practice, Innovation, IoT, Tech

Series – Part 4: Serverless Architecture – a practical implementation: Securing a Serverless REST API

In part three of the series I discussed creating a serverless REST API – using Lambda and API Gateway – to serve the collected IoT data from the security camera devices.

In this post I will cover securing the REST API.

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Data Architecture, Emerging Technology Practice, IoT, Tech

Serverless Architecture – Practical IoT Implementation

Since the series is getting quite lengthy I’m creating this post as a way to get access to the entire series.

In addition – I’ve created a Gitter room to discuss the GitHub Repositories and these posts. Just click on the link and join.

Posts in the Series:

Resources:

Data Architecture, Emerging Technology Practice, Innovation, IoT, Tech

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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Data Architecture, Emerging Technology Practice, Innovation, IoT, Tech

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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Data Architecture, Emerging Technology Practice, Innovation, IoT, Tech

Series – Part 1: Serverless Architecture – a practical implementation: IoT Device data collection, processing and user interface.

reinvent_launch-page_illustration_lambda — AWS Lambda

Serverless architectures are getting a lot of attention lately – and for good reason. I won’t rehash the definition of the architecture because Mike Roberts did a fine (and exhaustive) job over at MartinFowler.com.

However, practical illustrations of patterns and implementations are exceptionally hard to find. This series of posts will attempt to close that gap by providing both the purpose, design and implementation of a complete serverless application on Amazon Web Services.

Part 1 – The setup…

Every application needs a reason to exist – so before we dive into the patterns and implementation we should first discuss the purpose of the application.

Nest wants how much for cloud storage and playback?

I have 14 security cameras deployed, each captures video and still images when motion is detected. These videos and images are stored on premises – but getting them to “the cloud” is a must have – after all if someone breaks in and takes the drive they are stored on all the evidence is gone.

If I were to swap all of the cameras out for Nest cameras cloud storage and playback would cost $2250/year – clearly this can be done cheaper… so…

Continue reading “Series – Part 1: Serverless Architecture – a practical implementation: IoT Device data collection, processing and user interface.” →