Transforming the Industry
AI enabled cameras are poised to make a signifi cant 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 staffi ng, 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
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 signifi cant scalability, AI deployed
in edge devices such as cameras have limited computational power to identify and classify objects.
Once the algorithms have been pre-trained 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. AIbased
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 camerabased
AI will significantly improve and enhance security operations:
1. Identifying alert-worthy events
2. Enhanced Forensic Search Post-event
Identifying alert-worthy events. 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
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
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.
Forensic search post-event. 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
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, staffi ng data, schedule data, weather data, or many other
data sources. The potential for unifi ed data to create comprehensive
business solutions is substantial.
Future-Ready: Reducing Risk and
Cost in Migrating to AI in a
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 effi cient as possible.
Server-based AI defi nitely has its place in many use cases. The
greater compute resources available with server-based solutions
will allow workfl ows 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 effi ciency 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 2020 issue of Security Today.