Person Recognition: OpenCV vs. AWS Rekognition

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If you’ve been following along – I’ve been working with AWS Rekognition to detect people in security camera footage.

I have previous posts that discuss the results.

I’m now running the images through OpenCV using the pre-trained HOG + Linear SVM model. The picture in this post is an example of the output from OpenCV with a person detected and a bounding box drawn.

Over the next day or two I’ll start processing all the images with both Rekognition and OpenCV. I’ll also be capturing the results in Neo4j (where I’m already capturing the Rekognition object labels) to allow for comparative analysis.

Stay tuned…

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”

Social media has become an ad driven selection bias engine.

Image taken from the Wall Street Journal’s Blue Feed, Red Feed

Social media’s revenue is based on ads. You can only make money on ads by getting a huge audience and keeping that audience’s attention.

Show people what makes them feel good so they keep looking. Make sure they only see ideas that they can actively nod along with. Don’t make them think – or absorb ideas outside what makes them comfortable.

You’ll sell a whole lot of ads… and foment intellectual tribalism and hostility…

And if you are the “audience” it might be time to rethink how much of your time and intellect you allocate to a system specifically rigged to encourage bias.

P.S. I strongly recommend you click on the intellectual tribalism link.

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.

Continue reading “Data is the currency of your Digital Transformation”