Page 16 - Security Today, November/December 2024

C O V E R S T O R Y
“In an Edge AI model,
the majority of data
is stored in the cloud,
while only the most
frequently used
data is distributed
over a network to
the user’s fingertips.
This significantly
reduces bandwidth
and storage
requirements. ”
effective and phased approach for organi-
zations to incrementally adopt and deploy
AI capabilities in their video surveillance
and monitoring systems.
Edge AI may be better suited for appli-
cations that prioritize safety, effi cient op-
eration, and loss prevention, especially for
markets like banking, retail, government,
and military sectors that may have specifi c
regulations about camera performance
and data gathering.
Getting More out of
Your Tech Spend
Edge AI can allow for easier deployment, as
a company’s existing camera infrastructure
can be used without the need to replace ev-
ery camera all at once or invest in expensive
servers. The users still receive help from full
AI functionality and performance without
the potentially burdensome maintenance
costs associated with total camera replace-
ment or VMS licensing – while at the same
time getting the most use of their current
technology expenditures.
It is a similar transition strategy to
what the industry saw with the migration
from analog to IP, when many companies
used encoders to “convert” existing cam-
eras, ultimately getting more lifespan out
of current infrastructure without having
to do a complete upgrade all at once.
There are also more solutions avail-
able to make the logistics of performing
edge AI more cost-effective and effi cient.
1 6 Purpose-built AI hardware, like NVIDIA’s
Jetson devices, further enables deploying
powerful AI models on the edge. NVID-
IA’s Jetson platform can be used to run
specialized AI models and applications
tailored to the user’s needs. With Jetson,
customers can accelerate modern AI net-
works, easily roll out new detection mod-
els, and use the same software for different
products and applications.
For example, Hanwha multi-sensor
cameras feature an option to include an
NVIDIA Jetson module to run AI mod-
els. The Jetson is a small board that can
be inserted into a device and offers any-
where between 20 tops to 100 TOPS (Tera
Operations Per Second), a signifi cant up-
grade when added on top of the camera’s
original specs.
Hanwha’s AI Box also runs on the Jet-
son platform. The AI Box converts any
camera supporting ONVIF/SUNAPI
into an AI-enabled edge device with ob-
ject classifi cation and attribute extraction,
avoiding the added cost of “ripping and
replacing” an entire system all at once.
Devices like the AI Box are designed to
support AI apps developed by third par-
ties and targeted toward different verticals.
Customers who have those applications
can pick and choose new AI detections,
functionalities and analytics to load into a
device on a camera-by-camera basis. And
it is still done at the edge, allowing them to
use their existing infrastructure.
Customers can use solutions like these
to customize their edge networks, again,
without taking on the potentially signifi -
cant investment of overhauling their entire
infrastructure all at once. This approach
allows companies to pick the best camera
that suits a specifi c application and then
supercharge it with an add-on solution,
performing upgrades and replacements
slowly over time at a pace that makes sense
for their organization.
Even after a system has been deployed,
a company can evaluate it holistically.
Then they can make an informed deci-
sion about which cameras or views would
be the most benefi cial to receive added AI
capabilities whether they are looking for
analytics loitering, attribute extraction,
clothing or color.
When to Consider Cloud
From a hardware perspective, if a com-
pany is satisfi ed with its current hardware
infrastructure and does not plan to up-
grade anytime soon, edge can be an ideal
model. However, any hardware you buy is
limited to what it can process. Of course,
there will be improvements on the model
and potentially new detections added, but
it cannot compare to the cloud’s resources
and ability to deliver immediate updates.
It is important to weigh the pros and
cons based on organizational needs.
Again, hybrid solutions provide a great
deal of effi ciency since users can run most
of the needed AI models on the edge and
only pay subscriptions for high-processing
AI models.
A current hardware investment may
run certain object detection models per-
fectly well and will continue to do so
throughout the life of a device. But if
a company wants to run more models,
based on updated classifi cations, an edge
device may only be limited to its own abil-
ity as of the time of its original design.
Cloud-based AI offers access to a
deeper pool of resources and fl exibility,
and ongoing downloads of new fi rmware
and updates. Edge AI models also pro-
duce valuable metadata, which can be
uploaded to cloud-based platforms or
imported into AI metadata visualization
software programs. This helps generate
valuable business insights to support data-
driven decisions that can affect operations
across an entire organization – offering a
view into statistics that can optimize the
customer experience, increase productiv-
ity, enhance profi tability and more.
Companies today face increased se-
curity threats, and they are managing
their operations with fewer resources and
tighter budgets. They need options when
choosing the types of solutions that will
work best for them – and that solution
may be found on the edge, in the cloud or
a hybrid combination of
both.
Ramy Ayad Sr. is the
director of product
management at Hanwha
Vision America.
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