Video content analytics has always been a critical component
- By Stephanie Weagle
- Oct 01, 2018
Video surveillance has always been a critical component
in enabling security and ensuring public
safety: from security staff monitoring live feeds
to prevent incidents to officers combing through
recordings for video evidence to support investigations.
With the introduction of Video Content Analytics technology,
the value of video footage has become exponentially greater. The
ability to efficiently and effectively review and analyze video is a game
changer for law enforcement and private security agencies.
Video analytics solutions are enabling organizations to realize the
full value of their video surveillance resources and enhance safety,
security and overall business operations.
Overcoming Surveillance Challenges
with Video Content Analytics
From deterring crime, recording incidents and responding to threats,
video surveillance plays a central role in safety and security. While
security personnel utilize video predominately for its investigative
capabilities, these activities are still subject to human error and limitations.
It’s not unlikely, while monitoring one or multiple camera
feed, for an agent to lose focus or become distracted, missing a crucial
detail or object on camera as a result—not to mention the many man
hours it takes to review video content. Sometimes vital evidence captured
on video can’t even be detected by the human eye, regardless of
the attentiveness of the officer monitoring the recording or live feed.
This is where video analytics becomes an invaluable asset. Video
Content Analytics technology leverages an Artificial Intelligence (AI)
subspecialty called Computer Vision to train computers to detect
what humans cannot. Through Machine Learning, video analytics solutions
teach computers to detect and distinguish between video objects;
extract and identify them; analyze their behaviors and attributes;
and classify the data for multiple business and safety applications.
with Artificial Intelligence
For security purposes, AI-backed Video Content Analytics enables
live and recorded video footage to be processed and collated the extracted
information is then used to empower the human monitors
to make informed decisions based on data intelligence. With Video
Content Analytics, security personnel can search and filter video,
harness quantifiable data derived from footage and leverage actionable
insights to drive investigation efficiency.
To exemplify this, imagine a police force formulating its strategic
approach to video evidence as part of an ongoing investigation.
Video intelligence can provide critical support for law enforcement
teams building a case but allocating the resources to watching and
analyzing video is a major challenge. Reviewing video evidence requires
valuable time and manpower, and, often, investigative teams
must decide to rely less on time-consuming video intelligence to
better use the available resources. The police team might decide to
limit video investigation and only review footage from three cameras
focused on a single doorway—when they could derive much more
detail and accuracy by examining footage from ten cameras covering
an entire alleyway.
With a video analytics engine, the investigative work could be
dramatically shortened. If the security agent knows details about the
perpetrator being targeted, he or she can search the video, filtering
based on the suspect’s attributes and known features, and eliminate
from the video search all objects that don’t match the description.
Instead of watching hours of footage, the investigator can narrow
down the footage to the appearances of relevant objects similar to the
suspect, and quickly identify the necessary video evidence for building
By accelerating the video review process, police forces can dedicate
fewer officers to extracting video evidence, while collecting more
of it by expanding the scope of the video investigation.
In cases without a known suspect or little direction guiding the
video search, streamlining video review is even more critical. By detecting,
extracting and classifying video objects to understand the
context of a scene, AI-driven video analytics technology creates a
structured information database out of the unstructured video data.
Thus, investigators can quickly and comprehensively evaluate all video
events and respond based on actionable intelligence from surveillance
that otherwise would be underused.
Machine Learning for
Driving Real-time Response
Security doesn’t always involve post-event investigation—much of
the work behind ensuring public safety, revolves around responding
to events as they unfold. From uncovering a suspicious detail, to
identifying a potential threat and deploying responders, dangerous
incidents can be prevented, and security breach damage can be significantly
curtailed by real-time response.
Through Machine Learning techniques that train video analytics
to recognize patterns, Video Content Analytics solutions provide an
invaluable tool for detecting anomalies and suspicious behavior. With
AI-backed Video Content Analytics, law enforcement and security
professionals can configure notifications to be alerted when unusual
behavior may warrant their response. This is an essential capability
for managing access control, preventing trespassing and monitoring
loitering. When certain areas under surveillance are defined as sensitive,
a call to action can be triggered any time an object enters or
dwells in that area.
However, the security value can be extended even further for
emergency response. At a hospital, for instance, alerts can be configured
to notify security whenever ambulance access to the Emergency
Room is blocked. The same technology can be leveraged by a municipality
or local police force to ensure vehicles aren’t obstructing fire
hydrants or emergency vehicle access in public places. The ability to
proactively respond when a car is blocking the way of an emergency
vehicle can be the difference between a patient receiving emergency
treatment in time to save a life or the critical moments a fire fighting
team requires to combat a blaze before it spreads.
Extending Human Detection
with Computer Vision
While an attentive detective easily can notice abnormal behavior in a
live video feed, there are many details captured by video surveillance
that aren’t visible to the human eye. With Computer Vision technology,
these objects can be detected and indexed with the rest of the
video data by the Video Content Analytics engine. Whereas a security
officer in a control room monitoring a VMS may not be able to identify
a shadow or reflection of an object, a video analytics solution
could be configured to varying levels of detection sensitivity.
On the surface, a video may not prove the presence of a suspect at
a crime scene, but a video analytics engine, set to the highest degree
of detection sensitivity, might identify a perpetrator’s reflection and
extract it as a video object. While the criminal was outside the surveillance
range, if his or her reflection appeared in the surveillance footage,
it could be logged as video evidence to support a case.
This is not only true for detecting objects, but also for analyzing
events and drawing connections and conclusions about recorded data
and incidents. Machine Learning enables the collection and processing
of data in ways human analysts cannot, presenting it for easy
consumption and interpretation. With deeper data insights, security
personnel can use otherwise unutilized but valuable information for
Video Data-driven Decision Making
Video Content Analytics renders raw data into structured data.
When organized into dashboards and visualizations, the intelligence
easily can be analyzed by security forces. With an additional intelligence
layer, security personnel benefit from a high-level overview of
all surveilled objects and sites but also from the technology’s ability
to correlate between these data points and provide added insight.
For public safety officers, for example, this Smart City technology
can be leveraged to help drive traffic optimization. The Video Content
Analytics engine can identify the number of vehicles traveling
in every direction and understand high dwell time locations and durations.
Knowing that eastbound traffic experiences increased dwell
time at certain stoplights at specific times, the city can take action and
optimize traffic flows using the video surveillance infrastructure already
in place for monitoring the roads. Traffic control officials might
never have noticed the connection between all these data points, but,
when in the context of a dashboard visualization, this intelligence
can be leveraged to effect impactful change.
Video Content Analytics provide cities and local law enforcement
with the tools to optimize resident lifestyle beyond public safety. It
enables them to measure the efficacy of public infrastructure, transportation
services, and urban landscape. Another example of this
would be leveraging video data to make informed decisions and enable
intelligent planning of bike lanes, based on identifying the frequency
and high concentrations of bikers on main roads.
Airport security could leverage the same data visualization and
reporting capabilities for optimizing security screening processes.
Tracking patterns over time and understanding how the security
check points are navigated, the security and operations professionals
can identify the causes and locations of bottlenecks, prevent them
from forming and formulate contingency plans for overcrowding for
expected and unexpected influxes of people. Quantifiable data enables
organizations to plan based on trends and even A/B test solutions
to overcome challenges and meet internal and communal needs.
Video has always been a key sensor for enabling safety and security,
but Video Content Analytics introduce a deeper dimension
for harnessing the power of surveillance: operations optimization.
By offering access to data and the tools to respond productively,
proactively and predictively to situations, security
agencies can discover inefficiencies and their
causes, streamline investigations and emergency
response, and resolve diverse challenges as they
This article originally appeared in the October 2018 issue of Security Today.