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traffic flow and other objects of variable size and dwell time, and persons who move in and out of
the ROI set for associating them with the targeted objects.
o The function is vulnerable to the presence of multiple objects of similar size in the camera
field of view, target-to-background contrast, and the movement of persons in front of and
around the object that can grow quickly.
o Defining what constitutes an “object of interest” worthy of setting an alarm may be based
on criteria, such as the amount of time since the object last moved and on rules established
for object behavior.
o Associating objects with owners can be very complicated, especially when other
passengers are present and move in and out of the ROI. If a bag enters the terminal lobby
with two persons and one or both leave the bag at different times, and one returns but other
persons also appear in the ROI, the software will be challenged to associate “ownership”
with the proper person and establishing a rule for what constitutes an alarm will be similarly
o Theft Detection, Stationary Object Detection, and Loitering Object Detection are variations
of the Abandoned Object function, each with its own peculiarities.
Tailgating/Piggybacking Detection - detect one or more unauthorized persons moving through a
secured portal being used by an authorized person. Reliable detection will depend on camera
placement; scene illumination (to realize full camera performance); speed of passage and the size
of the gaps between persons; the displayed size of the persons relative to the screen size; and the
ability to discriminate between inanimate objects, such as ladders, which are carried through the
portal. Multiple cameras may be required to realize a satisfactory false alarm rate.
o False alarm problems are complicated by the inability to distinguish between a tailgating
event and a legitimate portal access. Two examples are a worker carrying a ladder and
maintenance personnel pushing a cleaning cart through a portal, both legitimate portal
accesses but not easily so identified by analytics software.
License Plate Recognition (LPR) – detect vehicles that make many passes or a single vehicle
already in your area by their license plate number. Many uses outside of traditional parking revenue,
especially for law enforcement purposes.
Facial recognition – as mentioned in Section 3, facial recognition software performance continues
to improve, to the point that this analytic is practical in many security applications. As a software
enhancement, facial recognition can be a cost-effective means of implementing two-factor
authentication of users when video cameras are already available. Video analytics come in several
types, including policy-based, rules-based, and behavior-based – these terms being relatively
loosely defined. The classes will often overlap, with each new class increasing detection
performance but also computational complexity (and cost).
Behavioral analytics, for example, intelligently monitor all the standard detected features of moving objects
and build up a concept, over a large period of time, of what motion can be deemed as typical, and thus can
be ignored. Events then become triggered by abnormal behavior which the airport operator should define.
A pedestrian loitering and/or approaching a number of different cars in a parking lot in a short period of
time, indicating potential criminal activity, could be viewed as abnormal behavior, and consequently
generating an event.
How the video analytics function is less important than what they deliver for airport security in the real-
world environment, whether they can be understood and used by the operators (with emphasis on false
alarms rates), and whether they can be adapted to the moves/adds/changes typical of an airport operation.
Video analytics are best applied to full resolution (4CIF) images at full frame rate (30 fps), because this
presents the greatest amount of information in the shortest time for analysis. Video analytics applied to CIF
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