Understanding AI in Video Surveillance
Applying human intelligence to computer programs
- By Brian Carle
- Jun 18, 2020
Many video surveillance professionals have come
across the terms Artificial Intelligence (AI),
Machine Learning (ML), and Deep Learning
(DL). But what do those terms mean, and how
do they affect Video Surveillance?
AI, MACHINE LEARNING AND DEEP LEARNING
AI is a term that loosely refers to applying human intelligence
to computer programs or allowing programs to learn over time
with the goal of producing better results as they learn. Machine
Learning is a technique used to achieve a level of AI, and Deep
Learning is an evolution of Machine Learning. In short, Deep
Learning is an advanced, more sophisticated Machine Learning
technique, and both are methods of achieving a level of AI.
Application in video surveillance. In video surveillance, video
analytics uses Machine Learning and Deep Learning methods to
identify objects, classify them, and determine their properties.
Whenever people receive new information, our brains attempt
to compare the data to similar items in order to make sense of
it. This comparative approach is the same concept that Machine
and Deep Learning algorithms employ.
Machine and Deep Learning algorithms differ in how they
are programmed to determine what constitutes a known object.
Machine Learning requires more human intervention from a
programmer to establish desired parameters in order to achieve
the desired outcome. Deep Learning identifies object attributes
independently and may consider characteristics the programmers
Machine learning versus deep learning. What do Machine
Learning and Deep Learning mean for Video Analytics? Both approaches
describe programming methods where a system learns
based on a data set. With Machine Learning, the attributes of the
data a system looks for are usually preset, or corrected for, by human
programmers. For instance, the system may be programmed
to delineate an object that is taller than it is wide, with limbs moving
in specified ways, and so on, and label this object a “person.”
Deep Learning is considered superior to Machine Learning, in
part because the programmers may not recognize the most relevant
criteria. Using the previous algorithm to identify a person, a seated
and stationary person may not trigger an accurate detection.
With Deep Learning, the video analytic algorithms are fed an
extensive data set representing an object. This step is called training,
where the algorithm trains itself to recognize a type of object.
For example, the system is fed thousands of images of people of
varying genders, styles of clothing, ethnic backgrounds, images
taken at different angles, and more.
The algorithm figures out attributes that are similar as well
as dissimilar, and also determines how to weigh the relevance of
those characteristics. After analyzing thousands of images, the
algorithm may calculate the majority of images include a triangular-
shaped object near the upper part of the image, with two
darkened oval spots near its bottom, which we would think of as
a nose on someone’s face. In fact, the algorithm may have identified many other such characteristics we wouldn’t think of.
Training the system is done by the developers of the software
before it is used by a consumer. The process takes a substantial
amount of computing power; much more than what is required
to detect and classify objects when used in the field. The result is
a file that is referenced by the system to determine if a detected
object matches the classification.
Because the Deep Learning process uses the machine to determine
object characteristics, it has led to analytics which can
provide much more granular classification. For instance, older
approaches may be able to detect a person, but Deep Learning
based analytics can detect whether the person is a man, woman,
or child. It may also be able to detect associated characteristics of
an individual as well as vehicle type or make.
Learning over time. Typically, AI in video surveillance is
trained at design time and, in some cases, does not get progressively
“smarter” when used in the field. Deep Learning and Machine
Learning do have this capability, however, and if used, can
employ analytics which can learn over time.
Typical applications may include systems that determine what
is normal in a scene. For instance, a school hallway experiences
a rush of traffic about every 45 minutes between class periods.
During that high traffic time, the traffic is dispersed and not concentrated
in any particular area.
Furthermore, it is unusual for all the people to be moving at a
very high speed. If the system detects an unusual concentration
of objects, it could indicate a fight broke out. If all the people
are running in the same direction outside of the usual inter-class
period, it could indicate an emergency situation.
SMARTER SYSTEMS, BETTER RESULTS
Video surveillance systems produce huge volumes of data. Monitoring
and filtering through such vast quantities of information
makes the task of quickly identifying security incidents and finding
evidence more difficult than ever.
Intelligent systems using Deep Learning can help us identify
evidence much more promptly and analyze
video in real-time to alert system operators of
suspected events, providing better results for
your security program.
This article originally appeared in the May/June 2020 issue of Security Today.