A look ahead – a different perspective

I’m sure you’re all reading many other “predictions” blogs this time of year focused on Artificial Intelligence, analytics, and the cloud. Certainly, those are the important topics to track. In this piece, however, we’ll go a few layers deeper into these areas, and hopefully bring to light some aspects of these subjects not commonly discussed.  

With the rapid changes occurring in our industry, the accelerated development of new technologies, and constantly shifting customer expectations, solutions providers need to always keep looking ahead, or risk falling behind perhaps permanently.  

Let’s start with Artificial Intelligence.

Across our industry we’ll continue to see more deployments of “smart” cameras and devices that combine AI and deep learning algorithms. The result will be more targeted forensic searches, enhanced operational efficiency, and minimized storage and bandwidth requirements.

At its most basic level, AI, and its subset technology machine learning, refer to the ability of “machines” to learn over time. Think of it like auto correct, or when Netflix recommends a movie you might like.

In surveillance it's well-proven that AI, especially when combined with deep learning, can produce more accurate searches that pick up on only the elements a security team needs to see. That’s simply about increasing accuracy through AI algorithms.

A term we should begin discussing more when it comes to AI is “context awareness.”

It’s already happening in other AI applications, for example, subtitling and captioning of movies or TV shows. Where basic AI models translate sentences one by one, context aware AI can “read between the lines and accurately considering the “context” of a conversation, language nuances and the subtle differences of gender, slang, and multiple word meanings.

We're already doing that in our surveillance products. Think about WiseStream III, our AI-based “’smart” compression and noise reduction technology. It can say to a user “Here’s this high-resolution camera footage, do you want to reduce bandwidth and manipulate the compression based on the objects you care about?”

Context awareness takes AI beyond the level of pre-configured algorithms. It’s making a device aware of what I care about and gives systems the ability to gather information about its environment and adapt its behavior accordingly. Now, the camera is making choices to optimize its performance based on what it has learned is important to the user.

When we first started introducing AI into our products a few years ago, it was done on a selective and specific basis. We could detect a person or vehicle by specific attributes and trigger an event, start recording or initiate a forensic search. That’s extremely useful, but it’s also AI 101.

Through ongoing firmware updates, our newer models are able to take that targeted AI detection and internally adjust what the camera is doing. Again, it’s context awareness.

It’s also a key element of our WiseDetector, which can train a camera on a custom object. If I have a manufacturing facility or a warehouse, and I want to know when a forklift goes by or if one of my robots has moved around the warehouse, then the camera can optimize internal settings – shutter speed, noise reduction, use of WiseStream – all based on what I care about.

Heavily wooded areas versus open areas could also be factors in determining context. Most people don't care much about trees swaying in the wind. They’re probably more focused on the fence line or only want to see people walking inside the perimeter. Because those trees are not AI objects that we care about, context awareness will prevent your security system from wasting bandwidth or optimizing shutter speed just for them.

It's the next level of AI’s ongoing learning about its surroundings.