I recently installed security cameras around my house which are doing an awesome job of recording all the events that take place around my house and grounds (generally of the feline variety).

Unfortunately the motion capture tends to be overly trigger happy and I end up with heaps of recordings of trees waving, clouds moving or insects flying past. It’s not a problem from a security perspective as I’m not missing any events, but it makes it harder to check the feed for noteworthy events during the day.

I decided I’d like to write some logic for processing the videos being generated and decided to write a proof of concept that sucks video out of the Ubiquiti Unifi Video server and then analyses it with Amazon Web Services new AI product “Rekognition” to identify interesting videos worthy of note.

What this means, is that I can now filter out all the noise from my motion recordings by doing image recognition and flagging the specific videos that feature events I consider interesting, such as footage featuring people or cats doing crazy things.

I’ve got a 20 minute talk about this system which you can watch below, introducing it’s capabilities and how I’m using the AWS Rekognition service to solve this problem. The talk was for the Wellington AWS Users Group, so it focuses a bit more on the AWS aspects of Rekognition and AWS architecture rather than the Unifi video integration side of things.

The software I wrote has two parts – “Detectatron” which is the backend Java service for processing each video and storing it in S3 after processing and the connector I wrote for integration with the Unifi Video service. These can be found at:

The code quality is rather poor right now – insufficient unit tests, bad structure and in need of a good refactor, but I wanted to get it up sooner rather than later… since perfection is always the enemy of just shipping something.

Note that whilst I’ve only added support for the product I use (Ubiquiti’s Unifi Video), I’ve designed it so that it’s pretty trivial to build other connectors for other platforms. I’d love to see contributions like connectors for Zone Minder and other popular open source or commercial platforms.

If you’re using Unifi Video, my connector will automatically mark any videos it deems as interesting as locked videos, for easy filtering using the native Unifi Video apps and web interface.

It also includes an S3 upload feature – given that I integrated with the Unifi Video software, it was a trivial step to extend it to also upload every video the system records into S3 within a few seconds for off-site retention. This performs really well, my on-prem NVR really struggled to keep up with uploads when using inotify + awscli to upload footage, but using my connector and Detectatron it has no issues keeping up with even high video rates.

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One Response to Detectatron

  1. Jethro Carr says:

    You might also find this interesting – a friend did a project using a Raspberry Pi, OpenCV and AWS Lambda to analyse a cat attempting to attack pet mice:

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