How they see you: A peek behind inner workings of AI surveillance

How they see you: A peek behind inner workings of AI surveillance

Understanding how large scale surveillance systems are driven on edge-computing

L arge scale intelligent surveillance systems used by governments and corporates have attracted a lot of bad press and public outrage over privacy and data security concerns. They often do data collection as well as data processing for extracting key insights regarding the scenes captured.

For example, these systems are alleged to do face recognition, matching, and information extraction of an individual or a group.

Data privacy and the ethical aspects of these surveillance systems are a whole other debate and beyond the scope of this article. What is more important is to be aware of what goes on behind building such a system.

Have you ever wondered how these systems work in real life? If yes, then I suggest you read on to find out.

I have been working on large scale machine vision systems for the past 2 years and through this post, I would like to share with you some insights I gained while working on some projects which required me to architect systems involving 200+ cameras that need to be processed in realtime using an AI backend.

The methods or knowledge I share are purely based on my learning and hands-on experience that I had and should not be considered as me saying its the absolute best practice or this is how every surveillance system out there works. Rather, it is a general template that showcases the potential of such pipelines and how the data gets handle in most scenarios.

So how does it all come together? To broadly put things to perspective, you have the cameras acting as the sensory aid for raw data capture, an artificial intelligence processing unit that could either be an edge computing device or a central processing server, a command center or control dashboard, typical networking infrastructure to stream the sensory data and a database to store all the vital info.