Transforming the Industry

Transforming the Industry

AI enabled cameras are poised to make a significant impact in security

Artificial Intelligence (AI) continues to gain momentum every day, and it is already poised to augment and enrich many aspects of our business and personal lives. For the security industry, and for video surveillance in particular, it’s clear that AI-based technology is about to make a substantial impact. Let’s examine some basic questions: How does it really work? What can it do for business? Is it going to be painful to migrate from non-AI cameras to AI-enabled ones?

According to a recent IHS Market Video Surveillance Installed Base Report, close to 85 million cameras will be installed in North America alone by 2021. But we cannot expect a proportional increase in security personnel to monitor and manually search through this vast amount of video. 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. And it can do so far more reliably than traditional video analytics ever did.

Dispelling Myths and Misconceptions

Correctly teaching an AI algorithm and ensuring its accuracy is done in a sophisticated process, even for server-based AI models and solutions. It is even more challenging to optimize deep learning models on the camera edge. Unlike a server implementation with far greater compute power, storage and database resources, or a cloud-based system with significant scalability, AI deployed in edge devices such as cameras have limited computational power to identify and classify objects.

Once the algorithms have been pretrained to identify certain objects and characteristics, they do not have the capability to “learn new things” by themselves. Additional capability is deployed by repeating the above process and deploying new firmware to the camera and its deep learning accelerator resources. This allows for a lighter approach to AI hardware resources on the camera, and still allows strong capability to deploy to the edge which can get stronger as it evolves.

A deep learning algorithm doesn’t see video in the same way we do, but it can look for familiar shapes and patterns that it has been trained to recognize. It can’t think for itself or make decisions that it hasn’t specifically been programmed to make. AI-based cameras aren’t inherently doing anything a human can’t do, but they are able to make associations and recognize certain objects exponentially faster.

Because an AI model doesn’t understand the context of a situation in the same way that people do for many use cases and complex situations, it will be quite some time before the technology can reliably decide and take action autonomously. It can, however, reliably show us events that are likely to be worth human attention, and when appropriate, feed the events to other systems to add more value.

Applications for Security

For security applications, there are two main areas where camera-based AI will significantly improve and enhance security operations:

1. Identifying alert-worthy events 2. Enhanced Forensic Search Post-event

Traditional video analytics that send alerts or tag motion events such as line crossing, loitering and object left behind are prone to errors and false positives from wind, rain, or people standing in front of the object in question. These previous generation video analytics only see “motion blobs” as opposed to objects with properties that allow them to be classified.

By using AI to detect and identify specific object types like people or vehicles, we can greatly reduce false alarms, while ignoring things like wind, rain, shadows and an errant plastic bag floating by. AI enables an entire new class of analytics, with more sophisticated logic and customization for precisely what an end user requires.

AI can also help us count objects like people or cars more precisely . This includes the ability to count objects accurately even when they partly “occlude” or pass in front of each other. This is key since it allows use cases like people counting from more sensible camera view angles. This is far beyond today’s video analytics, which require a top-down view to avoid occlusion, and which gives a less useful camera view when you want to see faces.

Beyond event triggers and object classification, it is important to realize how much descriptive metadata an AI-based camera can capture with each frame. And because the metadata is small, it adds very little to the overall bandwidth and storage requirements.

Several defining characteristics of a detected object can be captured such as the color of a person’s shirt and pants, length of garment, hat or no hat, glasses or not, handbag or not, and approximate age and gender. The impact on forensic search is profound. Imagine the time it takes to search through 10 hours of video looking for a man with a blue shirt and shorts. With the embedded metadata provided by an AI-based camera, the search yields results within seconds.

AI technology can even extract clues to behaviors like falling down or fighting by using human skeletal characteristics to classify how people are positioned. Being able to search through this embedded metadata on a VMS will require a plugin or API that can read the data. This capability will be available in Hanwha’s Wisenet WAVE VMS and plugins are also being developed for VMS systems such as Genetec’s Security Center that make integration easy.

Applications for Business Operations – Going Beyond Security

With all of this collected data, there’s never been a better time to start thinking about video cameras in a broader sense. AI-based cameras will become important data collection sensors which go far beyond simply capturing video.

They can identify and count objects, display heat maps and ensure process and operational compliance. As a result, they will become an invaluable tool for business operations. Depending on the business, the value proposition for such data can be a gamechanger worth many times the cost of the system. Cameras are already well accepted and commonplace, so the opportunities for them to evolve into unobtrusive, important data gathering tools for business and operations intelligence will only continue to grow.

Once seen as merely protecting the bottom line from loss, AI cameras can truly be seen as an enabling technology for revenue generation. They can be tools for operations and process measuring and metrics.

Bringing AI Data back from the Edge

With AI on the edge, the valuable events and other metadata generated by cameras will need to be gathered from many endpoints and the data aggregated together, usually in server or cloud-based systems, to enable clear visualization of the trends and anomalies identified. This can be presented via a suite of basic dashboard and trend visualization tools for a variety of customers.

For customers with more sophisticated needs or with various other types of data to be used in analysis, the camera metadata can be accessed and combined with other data and processed by other platforms for sophisticated visualization and data mining. This allows technology partners to access the data we aggregate into their own charts, graphs and exception reports powered by specialized software companies they may already be using.

There are familiar use cases spanning multiple industries that require linking data from access control, intrusion, point of sale systems, staffing data, schedule data, weather data, or many other data sources. The potential for unified data to create comprehensive business solutions is substantial.

Future-Ready: Reducing Risk and Cost in Migrating to AI in a Surveillance Environment

As AI-based cameras start out with only limited market share, it’s important to make upgrading to the technology as easy as possible for integrators. For this reason, Hanwha’s AI-based cameras will be based on magnetic camera modules that will quickly and easily snap in to any pre-existing Wisenet X series PLUS form factors. This will make migration from non-AI to AI as smooth and efficient as possible.

Server-based AI definitely has its place in many use cases. The greater compute resources available with server-based solutions will allow workflows not possible on camera hardware. However, because there are so many architectural advantages to edge-based computing on the camera, AI-based cameras are now poised to evolve the traditional security business, and the video endpoints it implements, into the IoT data-gathering business.

When talking to customers about AI, there is a clear desire for help with operations and processing needs. In many customers’ minds, AI represents a limitless potential to enhance business operations and logistics.

The video security industry now has the opportunity to reinvent itself as cameras and supporting infrastructure become smart sensors capable of assisting in day-to-day business operations and logistics. In addition to better serving traditional security detection use cases, AI-based cameras can gather information which directly impacts purposes beyond traditional security including revenue generation, operational efficiency and customer experience in unique and powerful ways. As such, they have the potential to transform our industry and open doors to new business opportunities like never before.

This article originally appeared in the March 2021 issue of Security Today.

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