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.