An article about intelligent CCTV, as published in the October 2017 edition of Aviation Security Magazine.

1. ALSO:
INTELLIGENT CCTV
KEY PERFORMANCE INDICATORS
IATA CONTROL AUTHORITIES WORKING GROUP
CENTRALISED IMAGE PROCESSING
SOUTH AMERICAN AIRPORT CRIME
THE GLOBAL JOURNAL OF AIRPORT & AIRLINE SECURITY
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Body Bombs:
cavity concealments
and surgical implants

2. F
or as long as the world of aviation
security has existed, the types of
threats it faces have evolved and
grown, as have the tools and processes
it employs to combat them. But CCTV
has always been a central component.
Since the first wave of aviation
related terrorist attacks in the 1970s,
we have seen a considerable increase
in the deployment of cameras, and
subsequently the construction of control
rooms and command centres.
As control rooms began to
evolve, three distinct generations
could be identified.
The first generation comprised of a
simple room with TV monitors and radio
communication equipment, located
wherever there was space. It was
more of an afterthought than a central
component and it was very difficult
to ensure monitoring standards (i.e.
protocols, response time, etc.).
Thesecondgenerationsawcentralised
control rooms, which were often well
planned and well built. They still had
their drawbacks; a lack of scalability, and
a technological inability to serve large
numbers of users.
The third generation saw the
emergence of complex integrated
systems, and centralised crisis
management centres, allowing the
appropriate authorities to have access
to video and data from multiple airports
simultaneously. This also allowed for
the creation of virtual control rooms
for multiple locations. Searchable video
became accessible to thousands of
users simultaneously (depending on
the manufacturer of the command and
control software).
Once the third generation made
its entrance, it was accompanied by
new, never before seen video analytical
capabilities such as Video Motion
Detection (VMD). VMD is one of the
oldest and simplest options, and so
when people think of analytics, they first
think of VMD for basic access control
(i.e. restricted area access and exit lane
monitoring), left objects and loitering –
we will return to this later in the article.
Even a person with the best intentions
cannot look at a video monitor for more
than 20 minutes without a deterioration
in their attention and a reduction in
their ability to detect events. This is
where video analytics come into play.
An ability to pre-empt incidents by
receiving notifications about suspicious
behaviour, enhanced forensic
capabilities and situation awareness
(location, identity and activity of
subjects and objects in a monitored
area) are just a few of the capabilities
introduced by this technology.
Basic analytics are now being used on
edge devices, i.e. they are built directly
into cameras or even video stream
encoders if older analogue cameras
were converted so they could be used in
a modern IP-based infrastructure.
Embedded analytics are a popular
option when minimal abilities are
required as they often include video
motion detection and loitering
detection. Without the use of a
sophisticated system, a condensed
video of all events in a given time
frame can be created, summarising a
24-hour period in 15 minutes or less,
thereby greatly reducing incident
review times.
The newest and most exciting
advanced video analytics capabilities are
based on machine learning (also referred
to as deep learning), which in turn is
a subset of cognitive computing, or in
more familiar terms, artificial intelligence.
Deep learning came out of the
concept of artificial neural nets, which
emerged three decades ago. When first
envisioned, the idea centred around
an attempt to replicate the human
brain, which has billions of neurons.
CAUGHTONCAMERA:
INTELLIGENT
CCTVINTHE
SPOTLIGHT
In a world of technological advancement
and futuristic marvels, intelligent CCTV
offers new opportunities for security
managers, with enhanced potential for
the detection of criminal acts or intent.
Eugene Gerstein offers an overview
of intelligent CCTV capabilities and
integration advice.
October/November 2017 Aviation Security International36

3. The early systems were too difficult to
programme, and the existing hardware
was too slow.
At the turn of the century, computing
capabilities reached a level at which
these sorts of applications became
feasible, allowing deep machine learning
to evolve rapidly.
In video surveillance, deep learning
contributes first and foremost to face
recognition – one of the most powerful
tools in the fight against crime and
terrorism. An organisation known as
the National Institute of Standards
and Technology (NIST) has been
working on a project called the Face
Recognition Vendor Test (FRVT) for
nearly two decades and, according to
a report published by NIST, in the last
20 years the error rates became three
times lower. Most face recognition
applications are based on technologies
such as Microsoft Azure Machine
Learning and Google Cloud Vision.
One of the highest standards in face
recognition is set by NEC, which has
consistently received top marks in the
Face Recognition Vendor Test (FRVT).
Another company specialising in face
recognition is Sagem, who is NEC’s
biggest competitor in this field. IBM
is a fairly new entrant in the security
space, having perfected their analytical
abilities and techniques in other
markets. Of course, there are many
other players in the field.
Face recognition is now being used
not only in security applications, but
also to index and retrieve images and
create user interfaces.
When looking at practical
applications in controlled
environments, i.e. passport control
desks, the accuracy of face recognition
applications has reached 99.9%.
In environments where there is less
control, there are multiple ways to
improve the probability of accurate
detection: with good-quality lighting,
and design with a holistic approach. For
example, positioning cameras towards
the top and bottom of a staircase allows
for greater success in face recognition,
as people have a tendency to glance in a
particular direction when reaching the top
or the bottom of stairs in a subconscious
‘levelling out’ action. Cameras hidden
inside advertisement billboards and flight
information displays are another excellent
tool, as subjects look at advertisements
subconsciously, and even ‘the bad guys’
look at flight schedules.
Human faces are the easiest ‘remote’
biometric markers as they are unique and,
unlike fingerprints and palms, they do not
require physical contact with biometric
identification devices. Additionally, they
can be obtained from a distance, thus
often making the acquisition covert. The
usage potential for such a marker is
tremendous, as the possibility of being
recorded has always served as a crime
deterrent – now being recorded can
often mean being identified.
Face recognition technologies, often
use something called Generalized
Matching Face Detection Method
(GMFD), which uses a specific algorithm
(called Generalized Learning Vector
Quantization – GLVQ) to generate pairs
of eyes, then search and select potential
‘candidates’ for a face match. Because
GVLQ is based on the concept of a
neural network, as discussed earlier, the
system adapts to changing conditions,
so it becomes difficult to confuse it
by attempting to prevent detection
by wearing sunglasses, baseball caps,
hoods, etc.
NEC, as an example of advanced
developments, has created something
called the Perturbation Space Method
(PSM). This algorithm is capable of
taking two-dimensional images (like
photos) and converting them into three
dimensions. Once the subject’s head is
rendered in three dimensions, it is then
rotated from left to right and then up and
down. Then it applies different lighting
to the face, from various angles, which
vastly increases the ability of matching
the resulting face to something in an
existing facial database. If early versions
of face recognition software were heavily
dependent on proper lighting, a lot has
improved in the last two years, with less
than optimal conditions still yielding
acceptable results.
Next came the recognition of facial
expressions, which originally served to
mitigate the effect of blinking eyes,
smiles, etc. during the matching
process. Nowadays it also works in
support of the nascent automated
behavioural recognition technology.
By noticing minute changes in facial
expressions, recognising patterns
of anxiety, nervousness and other
emotions, the system is able to notify
an operator about a potential threat.
Whilst it still has a long way to go,
we are not many years away from
an ability to accurately detect various
threats, and with the use of face
recognition and various other biometric
identification markers, to accurately
identify targets. For obvious reasons,
this precipitates a lengthy discussion on
privacy – something many civil liberties
organisations and many governments
are already engaged in.
“…positioning cameras towards
the top and bottom of a staircase
allows for greater success in face
recognition, as people have a
tendencyto glance in a particular
direction when reaching the
top or the bottom of stairs in a
subconscious ‘levelling
out’ action…”
“…NEC has created something
called the Perturbation
Space Method. This
algorithm is capable of taking
two-dimensional images and
converting them into three
dimensions…”
“…face recognition
technologies, often
use something called
Generalized Matching Face
Detection Method, which
uses a specific algorithm
called Generalized
Learning Vector
Quantization…”
October/November 2017 Aviation Security International 37EUR +44 (0)20 3892 3050 USA +1 920 214 0140 www.asi-mag.com

4. Another important facet of video
analytics is person and object
detection. Once again, deep machine
learning has shown considerable
promise in this segment. A large
visual database, called ImageNet
serves to allow various image software
algorithms to detect, then classify and
localise a database of over 14,000,000
photographs collected from various
search engines. The dataset employed
by the database contains thousands of
object categories. Many deep learning
systems are being trained using
ImageNet’s dataset, allowing them to
‘understand’ a tremendous number of
potential objects. The need for far
more accurate algorithms than was
previously available has led ImageNet
to create a contest called the Large
Scale Visual Recognition Challenge – it
is the best-known competition of its
kind to date.
A system with the appropriate
software can detect anything from
an unauthorised vehicle parked in a
restricted area, or moving erratically/in
the wrong direction, to people loitering
in specific areas, in groups exceeding a
set parameter, and so forth.
The interesting thing about deep
learning is that it leads to continuous
improvement–itisnotsetinstoneasitisn’t
a traditional computer algorithm, both in
the ‘scientific’ sense, and in the traditional
sense. It is capable of understanding
unexpected and unpredicted events;
things which are not clearly defined. In
other words, it is possible to say that this is
a nascent form of self-thinking (not proper
artificial intelligence, albeit the concept is
very similar).
An ability to understand the
aforementioned types of events can really
help with false alarms (or false-positives).
As a side note, License Plate
Recognition (an important application
for modern airports) is actually not best
served by deep learning, but rather by
traditional computer algorithms.
Another important intelligent CCTV
element is Video Content Analysis
(VCA), also known as Video Content
Analytics. This gives us the ability to
analyse video automatically in order to
detect various types of events.
VCA exists both as a software and as a
hardware capability, allowing for a wide
range of applications, including but not
limited to flame and smoke detection
and traditional security applications.
Video Motion Detection is one of
the simplest forms of VCA, detecting
motion as it appears against a stationary
background. VCA also allows for video
tracking (an excellent tool not only
for security, but also for marketing, to
analyse customer movement, etc.) and
egomotion, for example estimating a
moving car’s position in relation to road
signs, as seen from inside the vehicle.
Whilst useful for autonomous navigation
applications, it is also applicable to
mobile camera positioning.
VCA also supports other situational
awareness functionalities, such as
identification and behavioural analysis
(as opposed to face recognition for the
same purposes, as mentioned earlier).
One of the drawbacks of traditional
VCA is the absolute need for good
quality video, so it is usually supported
by various enhancements such as image
stabilisation and de-noising.
Video analytics can be used in many
interesting applications. For example,
waiting times can be reduced through
queue monitoring and passenger
counting, made possible with intelligent
queue management programmes
offered by companies like Xovis
(currently deployed at Zurich airport,
amongst others). This also applies to
monitoring baggage belts for people;
there was a well-documented case in
India where a first-time flier sat with his
baggage on the belt until he got to the
X-ray, which was the first time anyone
noticed him. Or, in Norway, there was
an incident in which an inebriated
passenger decided to go on a joy ride
around the baggage system.
Video surveillance goes hand in
hand with big data, and an airport
environment is a perfect ‘playground’
“…waiting times can be reduced
through queue monitoring and
passenger counting, made
possible with intelligent queue
management programmes
offered by companies like
Xovis…”
Vivotek loitering detection technology identifying object resting time of 10 ~ 180 seconds (Credit: Vivotek)
Xovis technology in use at Vienna Airport (Credit: Xovis)
October/November 2017 Aviation Security International38

5. for the acquisition and management of
such data because of the vast amounts
of people moving through. One of the
biggest problems currently facing high
traffic environments is the amount of
storage required for video, and the
complexity of searching this storage for
particular events.
Locating a particular person or a
vehicle in these data masses is not unlike
looking for a needle in a haystack. This
has given birth to a new breed of video
management systems, optimised for big
data searches, in a fashion similar to that
of a major search engine.
Manufacturers like Kipod, Milestone
Systems, Genetec, to name a few, are
offering a diverse range of solutions,
from cloud-based architecture
allowing for the use of thin clients
(such as your web browser instead of
specialised software) combined with
deep machine learning to a more
traditional video management system,
with local storage and dedicated
software clients.
Those at the forefront offer
considerable scalability and organic
growth to keep up with the evolving
airport environment. Its users can
search for data without preconfigured
rule sets as all live video features (i.e.
object trajectories) are recorded as
metadata, and thus easily searchable.
ICAO is fairly silent with regards to
intelligent CCTV, with the exception of
briefly mentioning video motion detection
(VMD).Thisallowsforgreaterfreedomwhen
developing solutions and deploying them in
a variety of airport-related applications.
Eugene Gerstein
is the business
d e v e l o p m e n t
director for
W e s t m i n s t e r
Aviation Security
Services Ltd, a
member of the
Westminster Group PLC. With over 20 years
of experience in 42 countries across the
globe, as well as being fluent in six languages,
Eugene has worked on large international
infrastructure projects, primarily in the airport
and defence industries, as well as having
spent years in AVSEC and ground operations.
With a background in engineering, Eugene
has developed perimeter intrusion detection
products and services, matching exacting
client specifications. Prior to his commercial
activities, Eugene was a military officer
and served in law enforcement, involved
in counter-narcotics and counter-terrorism
task forces. He can be contacted at
e.gerstein@wass-ltd.com.
Recognize AND Analyze
www.cognitec.com • info@cognitec.com
Premier face recognition technology for real-time video
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Is this frequent
traveler Sarah
Jones?
How old is he?
Is she an
authorized
employee?
How often
was she here
this month?
When, where
did she enter?
Is he on this watchlist?
Are there too many people in line? What is their average check-in time?
October/November 2017 Aviation Security International 39EUR +44 (0)20 3892 3050 USA +1 920 214 0140 www.asi-mag.com