Page 40 - Security Today, September/October 2021
P. 40

The Time Has Come Artificial intelligence is the talk of the town in most industries
By Aaron Saks
Every technology industry is talking about the benefits of Artificial Intelligence. More than a buzzword, AI is hyped as a panacea, while at the same time, it is often misunderstood by those who might benefit from it the most.
AI may mean different things to different people, there are plenty of aspects that apply to all disciplines. The ability for a machine to “learn” from data it is presented is at the core of all AI use cases. The term “machine learning” derives from that most basic idea. Deep Learning, a subset of machine learning, and is based on neural networks, and is frequently used to analyze and compare image data. The challenge is how to use these AI disciplines effectively to better protect people and assets. Beyond security, it’s time to look at how data from these cameras can be used to positively impact operations and sales for an organization.
Traditional digital cameras do not identify objects they capture. They just blindly record pixels to a disk. With analytics, if the camera sensor detects movement in those pixels, it can place a bookmark in the recording or send an alert. Anyone who has tried to use traditional motion analytics, although they can be useful, will know they are also very prone to false positives. Depending on the installation, a motion event is triggered by something as mundane as a shadow from a passing cloud. For this reason, many security professionals shied away from using analytics in all but the most controlled circumstances or as a guide to where an event “might” have happened.
Using deep learning algorithms, we can effectively teach a camera sensor to identify objects and detect unique characteristics about them. It is a sophisticated process to train a machine learning algorithm and it can require hundreds of thousands of images to make it accurate. The algorithms must be told when it gets things wrong, as
well.Itisalsoimportanttorememberthatwhatdifferentiatestoday’s technology from true AI is that machine learning and deep learning algorithms cannot learn new things by themselves.
Current AI-based cameras can reliably identify objects such as a car, truck, bicycle, license plate or a person in an image. They can also discern the unique attributes of these objects, such as color or whether a license plate or face is present. Thanks to advances in deep learning, these devices have evolved from capturing images to becoming highly accurate data gathering tools. They are network connected, and are truly part of the broader world of IoT devices that surround us. With their myriad new potential to protect and inform, it’s time to think differently with regards to the value these devices can bring to an organization.
The most common application for AI-powered cameras today is to empower the traditional motion analytics that we are familiar with, such as loitering, intruder detection or entering/exiting an area. AI becomes a powerhouse when used to eliminate false positives from shadows, foliage or animals, by only triggering the analytics when the correct type of object is detected, such as a person or vehicle. The bar is raised further when a deeply integrated AI solution allows additional descriptive metadata search parameters to speed forensic investigations, such as searching for clothing color, or if the subject had a bag, glasses or hat.
AI presents a perfect solution to compensate for unmanned environments or those with limited staffing, or the loss of vigilance after looking at a screen too long. AI can help us not only watch continuously, but also feed systems that are able to sort, organize and categorize massive amounts of data in a way that human operators cannot. It can do so far more reliably than traditional video analytics ever did.
Kryuchka Yaroslav/

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