Deep learning and edge computing... they're more than just buzzwords
Your world is entirely mobile. Everything from checking emails, ordering your morning Starbucks, booking a flight and walking your dog can be done from your mobile device.
This is all thanks to edge computing and edge devices.
Security is beginning to catch up to widespread technology trends with the adoption of IoT devices and leveraging A.I. to automate monotonous tasks.
Break it down for me
Let's define some important terms:
Edge computing: a distributed computing structure where computations are performed on distributed device nodes (edge/IoT devices) as opposed to primarily taking place in a centralized cloud environment.
Edge device: a smart device that provides an entry point into a core network, it acts as a checkpoint at the border between two countries (thank you Computer Hope for that perfect metaphor).
Deep learning: a function of artificial intelligence that copies the functions of the human brain by processing data and creating patterns for use in decision making.
The advent of edge technology has allowed deep learning to become a cost-effective solution in most applications. Video analytics are a perfect example. It's the leading trend in surveillance technology because it automates the most monotonous tasks that are prone to human error.
1. Data Safety
Without edge computing, your data has to be relayed through several channels, sent to a third party location, and processed by a third party GPU server.
With so many steps in the data transfer process, data safety cannot be guaranteed. It's similar to checking your bags at the airport: you always run the risk of your luggage being lost, but that risk is much higher when you have several flight connections. It's the same with data transmissions: the more connections, channels, and data, the higher the risk of your data not being properly delivered to its destination.
Edge computing can run the necessary processing locally, which produces faster, more reliable results.
2. Network Speed
If you've ever gone over your monthly data limit on your phone bill, then you know that video demands a lot of network bandwidth.
Transmitting 1080p video to the cloud consumes roughly 1 GB per hour per camera. Imagine how much bandwidth a facility with 45 cameras would consume streaming all that footage to the cloud (hint: it's a lot).
Oftentimes, clients don't have enough network bandwidth to continuously upload this data to the cloud. Edge computing allows for video analytics to be processed locally without any transmission necessary. This keeps your networks running at top speed and peak performance.
3. Calculation Cost
Running robust video analytics is no small feat.
For this level of computer vision, you want plenty of computing power to complete necessary calculations. This is why it's critical to look for devices that are installed at the edge.
Traditionally, video processing and rendering is done using an expensive GPU. Some solutions, like GPU cameras for instance, can run machine learning algorithms right on the device. Although, GPU cameras can be expensive and can't always keep pace with regular CPUs.
Thankfully, computing power doesn't have to come at a high cost. With edge processing, mini-computers can be installed in tandem with an existing camera. This allows the camera to stream video directly to the mini-computer, which runs the deep learning algorithms in real-time.
This solution offers the best of both worlds: pre-existing cameras can work with partnered CPUs to run complex computer vision programs.
If you're curious about a solution like this, check out our AiVR.
4. Deep Learning versus Not
Why is deep learning a big deal?
Here's the short answer: video analytics without deep learning capabilities is about as helpful as a screen door on a submarine.
Here's the long answer: deep learning means that machines are learning data representations rather than task-specific algorithms (once again, thank you to Wikipedia for that succinct description). It's the difference between your camera merely detecting motion versus knowing the difference between a person and a dog or a cup of coffee and a cell phone.
Devices that aren't equipped with machine learning technology detect activity based on motion. This can result in hundreds of false positives being reported each day and plenty of wasted time.
Pairing edge devices and deep learning algorithms creates a dynamic, cost-effective security solution. This is becoming a newly-adopted method of automation throughout the security industry because of its clear advantages.
To learn more about edge devices for your security system, download our AiVR specs to see some solutions in action.