Here are some selected excerpts from the full article published in Security Business magazine, written by Ray Bernard. Read the illustrative examples and other informative details here: https://www.securityinfowatch.com/integrators/article/21070746/partners-in-profit-ai-and-rmr
AI-enhanced video analytics stand to transform the video landscape to an extent not previously conceived. What we’ve seen so far is just the tip of the iceberg.
Subscription-based offerings will fit AI best because today’s AI software is being developed under the continuous delivery model, an engineering approach for providing applications that evolve in place through updates delivered every week or so – much like what we see happening with our smartphones and with the model that IT has shifted to.
Why Security Industry AI Decisions are Easier
It is important to realize that when it comes to AI for video surveillance applications, several factors make the evaluations and decisions easier than for AI applications in other industries:
- Video analytics are visual.
- AI is transforming security technology beyond its protective purposes into active sources of business-relevant real-time data.
- Security technology AI concepts are understandable.
- The number of AI-savvy people is growing quickly.
- High-performance computing hardware for AI is finally available for on-premises deployments.
Machine learning is the science of getting computers to perform actions without specifically being programmed to do so. For example, machine learning software for email spam filtering would be “trained” on recognizing spam by being fed thousands of emails labeled either as spam or not spam, and the software would analyze each email and determine from those examples how to identify spam.
Deep learning is a type of machine learning that involves artificial neural networks, whose designs are inspired by the way that scientists believe the brain works. A neural network is built from pieces of software called “nodes” – which are organized into layers. Each layer performs a step in the data processing, passing along its results from one layer to the next. Deep learning software typically contains three parts: an input layer, hidden layers and an output layer.
The term “deep” refers to neural network software that has many hidden layers – the number of layers determining the depth. A simple neural network has one or two hidden layers between the input and output layers; three or more hidden layers makes it a deep learning neural network.
Cameras on a Mission
Current AI research by Milestone Systems is applying context in a different way – using deep learning to automatically adjust camera configuration in real time to optimize camera settings based on the camera’s purpose.
At the 2019 Milestone Integration Platform Symposium (MIPS) event, about six minutes into his Day 1 presentation, Milestone’s Director of Research Barry Norton provided example video for a camera whose purpose is to perform license plate recognition (LPR). The demonstration used two Canon VB-S900F cameras, both initially configured optimally for best general performance using on-camera settings. Then one of the cameras activated server-based AI to constantly adjust for best contrast, lack of glare, and lack of motion blur – creating a startling difference between two versions of the same low-light scene. Obtaining this level of camera performance around the clock is not possible with on-camera configuration settings.
AI and RMR: Perfect Partners
Cities are already deploying AI technologies for public safety and security. In Stanford’s 2016 AI Index report, the authors concluded that by 2030, the typical North American city will rely heavily on AI technologies, including cameras. Although not all types of AI perform as well as others, there are AI-based quality improvements that make AI camera analytics much more effective than ever. Such analytics also apply beyond smart city use cases into many business and industry sectors.
AI-based technologies are typically offered under an “as-a-service” monthly-subscription model – whether the AI computing is done on the cloud or on premises. Typically, that delivery model results in customers expanding their subscriptions year over year due to the increasing value of the new features.
It has been an uphill journey because of the mismatch between 20th century security industry practices and the low number of as-a-service offerings available; however, reaching the summit where AI and RMR meet is within sight.