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Classic Vision
Separated system for capturing
image streams and processing,
outsourced analysis for decisions,
made to support many possible tasks.
Embedded Vision
Fully integrated all-in-one system
capturing image streams, on-board
processing and interpreting data,
autonomous
decision
making,
resulting in an action. Made for a
specific task.
How IoT applications benefit
from Embedded Vision
Embedded Vision does not only take
pass / fail or yes / no decisions based
on some criteria, it provides a broader
form of intelligence leveraging neural
networks that massage and analyze
the image data and information.
Embedded Vision systems moves
from pre-defined actions based on
specific inputs to specific reactions
to spontaneous situations, with real-
time decision making and resulting
activities. This is similar to how smart
cameras work but allow for adaptation
and expansion with evolving responses
as more scenarios are encountered
and evaluated. Also, the “smarts” are
being deeply integrated into every kind
of device, more and more. This creates
new IoT devices that are more aware
and better able to process inputs from
their surroundings, further propagating
how Embedded Vision is enabling more
VoT devices.
Most industrial and consumer products
are internet-aware today, exchanging
data with one and other using local
networks and the cloud. With the
addition of vision, these devices will
be controllable via eye tracking, face
or gesture recognition. As an example,
a refrigerator with embedded vision
inside would be able to recognize what
food has been consumed and then
automatically add it to the family’s
online shopping list. Using intelligent
embedded vision, security applications
can count people, create heat maps,
or identify persons of interest and
share the visual and analytics data
within networks. When it comes to self-
driving cars, embedded vision steers
the vehicle within its lane on the road
and avoid obstacles that may appear
without any warning. This example
highlights the importance of Embedded
Vision in this application to not only to
see but to understand the scene and
react accordingly.
From a technical perspective, a smart
embedded vision system not only
recognizes defects or abnormalities
based on pre-defined criteria, it is
capable of determining an appropriate
response to correct or avoid them.
Embedded Vision provides a more
comprehensive view of the world
by recognizing, understanding and
identifying the environment without
further external interaction.
What is required for a fully
embedded vision product?
It is all about efficiency. Embedded
Vision brings vision technology to its
simplest formula “capture, process,
respond”. The building blocks of a true
embedded vision systems are:
Sensor or Sensor Module
Control unit to receive the images
and direct them to the processing unit
Processing unit that is either local
or cloud based that provides the full
image pipe line
Purpose build algorithms and neural
networks that provide the intelligent
processing of the images
Embedded Vision requires more
analysis and processing of the image
data, so an embedded vision product
typically includes back-end processing
that is done on either an ISP or GPU.
Intelligent algorithms running on these
devices allow the machine to analyze
the incoming video data, process them
and interpret them to make decisions
and react accordingly. Embedded vision
products provide not only data but
results based on this data.
“Embedded Vision does not
have to be small but it has
to be smart.” –
Darren Bessette
Typical embedded vision components
come in small form factors, but this does
not mean that they can only be used in
small or low-cost devices. As with the
example provided previously, self-driving
cars are the exception to using embedded
vision in small, low cost devices. A better
way to think of embedded vision is to
think of smart imaging that do not need
human interaction to process and react
to the video stream. Embedded Vision
is enabling machines to see and think,
powering the Vision of Things of the
future.
Figure 1:
Embedded vision systems
typically combine a camera, processing
device and interface
Embedded vision examples: Home
robotics, e. g. robot vacuum cleaners
Darren Bessette, Category
Manager and machine
vision expert, FRAMOS
New-Tech Magazine Europe l 65