Make sure you have the right camera placement for optimal use of analytics
- By Ian Westmacott
- Aug 01, 2015
Those words are often associated with the real estate industry, but the
security community can also apply that mantra to the all-important
issue of camera placement for optimizing analytics.
One of the major knocks on analytics since its introduction has
been that it doesn’t always deliver on expectations. End users want to
gather all sorts of data ranging from license plate recognition to people counting
to dwell and linger analysis, yet the results can be inconsistent.
The biggest culprit in creating patchy outcomes is improper camera selection
and positioning. If this isn’t handled properly from the onset, the results from the
analytics provided by these cameras will never meet expectations. No matter what
else you do, if you mount a camera overhead and then decide you want it to read
license plates, it can’t do the job. Similarly, a camera that is set up in the dead of
winter near a parking lot surrounded by trees won’t be of much use when summer
comes and the trees have grown and are now obscuring views, casting shadows and
creating interfering movement.
Getting the most out of an analytics system has to begin long before the first
images are captured. It starts with knowing what you want the analytics to do. This
will then help determine the types and numbers of cameras used, the mounts on
which they are installed, and the way they are positioned within various settings.
Fortunately, there are professionals within the manufacturer and integrator
communities that can walk an end user through the process of selecting and installing
cameras to maximize the analytics output. But, it is also important to
know what the potential issues are for the different scenarios so you can make
There are many factors that impact video quality—illumination, the size of the
asset, separating assets from people, obstructions, environmental issues and movement.
We’ll take a close look at each of these and how proper camera positioning
can work around them.
Light is considered a good thing, right? However, too much light in the video
frame, such as in the form of headlight glare or reflection from a body of water or
glass, and suddenly the analytics within the camera can miss an event or misinterpret
it as a false alarm.
To avoid this issue, the camera needs
to be positioned so that it isn’t pointed
at the source of the reflection, such as
a store window or mirror. Polarizing
filters can be added to the camera lens
to deal with this, while thermal cameras
can also be part of the solution.
Headlights, brake lights and any
other type of moving light can cause
blooming, which stops analytics from
monitoring a situation. There are a
couple of solutions to this problem,
one of which is to use thermal cameras.
Another is to position the camera so it
captures video beside the vehicles, rather
than in front of it.
While too much light can be a detriment
to analytics, so too can insufficient
light. Thus, it is important when
positioning cameras in potential lowlight
areas to consider using thermal or
integrated day/night cameras as well as
to explore ways to use artificial light to
illuminate the area.
Shadows from trees, clouds or people
can cause analytics to misinterpret
activity, triggering alarms when none
has occurred or, if the activity happens
within the shaded area, it may not be
visible enough for analytics to see it.
Better lighting and thermal cameras
are two options for improving this scenario
Size of the Asset
Analytics likes to track bigger objects,
so making assets a larger portion of the
camera view will ensure that they are
With a fixed asset, such as a shelf
full of merchandise, the best way to get
the proper view is to move the camera
closer or to adjust the zoom so the merchandise
represents at least 5 percent of
the camera view.
While analytics can distinguish people
from product, over time if the two occupy
the same space within the camera
view it becomes harder for the software
to separate them. A person standing
still in front of a shelf filled with merchandise,
for example, could be mistaken
as part of the display.
When he moves, it looks as if something
is being stolen. Correct camera
placement and proper use of analytics
tools, such as drawing a detection
region that includes the assets but excludes
the people, can address this.
There are few scenarios that allow cameras
to have unobstructed views from
all angles, so it may be necessary to
consider multiple cameras positioned
for wide angle, overhead or other views
to cover an area appropriately.
When placing the camera, you
want to avoid blocking the view, either
with fixtures, furniture, shrubbery or
even people. At a cash register, rather
than pointing a camera so it provides
a head-on, blocking view, it’s better
to position it with a sidelong shot
that sees both the employee and the customer without obstruction.
Weather certainly plays a role in how
analytics works. If there is constant
movement from wind, rain or snow in
a scene it can trigger false alarms. It’s
best to avoid placement where moving
branches are likely or where precipitation
can directly affect the view.
Ultimately, we can’t control the
weather and often we can’t control the
environment either. In these cases the
best we can do is to understand what
impact the environment will have on
the analytics program.
Like trees swaying in the wind, all moving
objects can cause issues. Therefore,
when placing cameras, think about
their proximity to anything that provides
constant motion—escalators, automatic
doors, even traffic that can be
viewed through windows. If possible,
aim the cameras so that uninteresting
motion is not in the camera’s field of
view. If this is not possible, then use detection
regions to limit the analytics to
areas away from the constant motion.
Having addressed the possible problems
that can impact video quality,
another factor to consider with camera
placement is what you’re hoping to
achieve with your analytics.
An activity such as people counting
works best on a two-dimensional scale,
so a camera mounted overhead works
better than one positioned at eye level.
With a straight on view, people coming
into a building, such as those entering a
stadium lobby might block others from
the camera view. Positioned overhead,
however, the camera can identify each
Dwell and linger is another great
analytics tool that can be enhanced by
proper camera placement. A retailer
who wants to measure how many people
look at a display and how long they
stand there would need cameras that
have an unobstructed view as well as
one that provides enough space around
the display to fully capture people as
they move near it.
Think not only about what you
want to achieve immediately, but also
consider how needs may change down
the road. If surveillance is the current
plan, but at some point license plate
recognition is a goal, consider sourcing
and placing cameras with both needs in
mind. While this is not always possible,
a little planning up-front can often help
down the road.
The science of analytics relies very
much on the art of camera placement.
But if the two are considered together
from planning through execution the
outcome will be a successful one.
This article originally appeared in the August 2015 issue of Security Today.