[News] The Rise of Smart Camera Networks, and Why We Should Ban Them
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Mon Jan 27 13:54:20 EST 2020
The Rise of Smart Camera Networks, and Why We Should Ban Them
Michael Kwet - January 27, 2020
_There’s widespread concern_ that video cameras will use facial
recognition software to track our every public move. Far less remarked
upon — but every bit as alarming — is the exponential expansion of
“smart” video surveillance networks.
Private businesses and homes are starting to plug their cameras into
police networks, and rapid advances in artificial intelligence are
investing closed-circuit television, or CCTV, networks with the power
for total public surveillance. In the not-so-distant future, police
forces, stores, and city administrators hope to film your every move —
and interpret it using video analytics.
The rise of all-seeing smart camera networks is an alarming development
that threatens civil rights and liberties throughout the world. Law
enforcement agencies have a long history of using surveillance against
marginalized communities, and studies show surveillance chills freedom
of expression — ill effects that could spread as camera networks grow
larger and more sophisticated.
To understand the situation we’re facing, we have to understand the rise
of the video surveillance industrial complex — its history, its power
players, and its future trajectory. It begins with the proliferation of
cameras for police and security, and ends with a powerful new industry
imperative: complete visual surveillance of public space.
Video Management Systems and Plug-in Surveillance Networks
In their first decades of existence, CCTV cameras were low-resolution
analog devices that recorded onto tapes. Businesses or city authorities
deployed them to film a small area of interest. Few cameras were placed
in pubic, and the power to track people was limited: If police wanted to
pursue a person of interest, they had to spend hours collecting footage
by foot from nearby locations.
In the late 1990s, video surveillance became more advanced. A company
called Axis Communications invented the first internet-enabled
converted moving images to digital data. New businesses like Milestone
Systems built Video Management Systems, or VMS, to organize video
information into databases. VMS providers created new features like
motion sensor technology that alerted guards when a person was caught on
camera in a restricted area.
As time marched on, video surveillance spread. On one account
about 50 years ago, the United Kingdom had somewhere north of 60
permanent CCTV cameras installed nationwide. Today, the U.K. has over 6
million <https://www.bbc.com/news/uk-30978995> such devices, while the
U.S. has tens of millions
According to marketing firm IHS Markit, 1 billion cameras
will be watching the world by the end of 2021, with the United States
rivaling China’s per person camera penetration rate
Police can now track people across multiple cameras from a
command-and-control center, desktop, or smartphone.
While it is possible to link thousands of cameras in a VMS, it is also
expensive. To increase the amount of CCTVs available, cities recently
came up with a clever hack: encouraging businesses and residents to
place privately owned cameras on their police network — what I call
“plug-in surveillance networks.”
Video from surveillance cameras around the city is displayed at the Real
Time Crime Center the viewing space for Project Green Light, at the
Police Department's headquarters in downtown Detroit, June 14, 2019. In
recent weeks, a public outcry has erupted over the facial recognition
program employed in conjunction with the network of cameras. (Brittany
Greeson/The New York Times)
Video from surveillance cameras around the city is displayed at the
Real-Time Crime Center, the viewing space for Project Green Light, at
the police department headquarters in Detroit on June 14, 2019.
Photo: Brittany Greeson/The New York Times via Redux
By pooling city-owned cameras with privately owned cameras, policing
experts say an agency in a typical large city may amass hundreds of
thousands <https://www.rand.org/pubs/research_reports/RR2619.html> of
video feeds in just a few years.
Detroit has popularized plug-in surveillance networks through its
controversial Project Green Light program. With Project Green Light,
businesses can purchase CCTV cameras and connect them to police
headquarters. They can also place a bright green light next to the
cameras to indicate they are part of the police network. The project
claims to deter crime by signaling to residents: The police are watching
Detroit is not alone. Chicago
Orleans <https://www.safecamnola.com/>, New York
also deployed plug-in surveillance networks. In these cities, private
businesses and/or homes provide feeds that are integrated into crime
centers so that police can access live streams and recorded footage. The
police department in New Haven, Connecticut, told me they are looking
into plug-in surveillance, and others are likely considering it.
The number of cameras on police networks now range from tens of
thousands (Chicago) to several hundred (New Orleans). With so many
cameras in place, and only a small team of officers to watch them, law
enforcement agencies face a new challenge: How do you make sense of all
The answer is video analytics.
Video Analytics Takes Off
Around 2006, a young Israeli woman was recording family videos every
weekend, but as a student and parent, she didn’t have time to watch
them. A computer scientist at her university, Professor Shmuel Peleg,
told me he tried to create a solution for her: He would take a long
video and condense the interesting activity into a short video clip.
His solution failed: It only worked on stationary cameras, and the
student’s video camera was moving when she filmed her family.
Peleg soon found another use case in the surveillance industry, which
relies on stationary cameras. His solution became BriefCam, a video
analytics firm that can summarize video footage from a scene across time
so that investigators can view all relevant footage in a short space of
Using a feature called Video Synopsis, BriefCam overlays footage of
events happening at different timesas if they are appearing
example, if several people walked past a camera at 12:30 p.m., 12:40
p.m., and 12:50 p.m., BriefCam will aggregate their images into a single
scene. Investigators can view all footage of interest from a given day
in minutes instead of hours.
Thanks to rapid advances in artificial intelligence, summarization is
just one feature in BriefCam’s product line and the rapidly expanding
video analytics industry
Behavior recognition includes video analytics capabilities like fight
detection <https://www.youtube.com/watch?v=QcCjmWwEUgg>, emotion
recognition, fall detection, loitering, dog walking, jaywalking, toll
fare evasion, and even lie detection
Object recognition can recognize faces, animals, cars, weapons, fires,
and other things, as well as human characteristics like gender, age, and
Anomalous or unusual behavior detection works by recording a fixed area
for a period of time — say, 30 days — and determining “normal” behavior
for that scene. If the camera sees something unusual — say, a person
running down a street at 3:00 a.m. — it will flag the incident for
Video analytics systems can analyze and search across real-time streams
or recorded footage. They can also isolate individuals or objects as
they traverse a smart camera network.
Chicago; New Orleans; Detroit; Springfield, Massachusetts; and Hartford,
Connecticut, are some of the cities currently using BriefCam for policing.
To Search and Surveil
With city spaces blanketed in cameras, and video analytics to make sense
of them, law enforcement agencies gain the capacity to record and
analyze everything, all the time. This provides authorities the power to
index and search a vast database of objects, behaviors, and anomalous
In Connecticut, police have used video analytics to identify or monitor
known or suspected drug dealers. Sergeant Johnmichael O’Hare, former
Director of the Hartford Real-Time Crime Center, recently demonstrated
<https://www.youtube.com/watch?v=lDC3EVAMvYI> how BriefCam helped
Hartford police reveal “where people go the most” in the space of 24
hours by viewing footage condensed and summarized in just nine minutes.
Using a feature called “pathways,” he discovered hundreds of people
visiting just two houses on the street and secured a search warrant to
verify that they were drug houses.
Video analytics startup Voxel51 is also adding more sophisticated
searching to the mix. Co-founded by Jason Corso, a professor of
electrical engineering and computer science at the University of
Michigan, the company offers a platform for video processing and
Corso told me his company hopes to offer the first system where people
can “search based on semantic content about their data, such as, ‘I want
to find all the video clips that have more than 3-way intersections …
with at least 20 vehicles during daylight.’” Voxel51 “tries to make that
possible” by taking video footage and “turning it into structured
searchable data across different types of platforms.”
Unlike BriefCam, which analyzes video using nothing but its own
software, Voxel51 offers an open platform which allows third parties to
add their own analytics models. If the platform succeeds, it will
supercharge the ability to search and surveil public spaces.
Corso told me his company is working on a pilot project
with the Baltimore police for their CitiWatch surveillance program and
plans to trial the software with the Houston Police Department.
As cities start deploying a wide range of monitoring devices from the
so-called internet of things, researchers are also developing a
technique known as v
<https://www.rand.org/pubs/research_reports/RR2619.html>, or VA/SF, for
police intelligence. With VA/SF, multiple streams from sensors are
combined with video analytics to reduce uncertainties and make
inferences about complex situations. As one example, Peleg told me
BriefCam is developing in-camera audio analytics that uses microphones
to discern actions that may confuse AI systems, such as whether people
are fighting or dancing.
VMSs also offer smart integration across technologies. Former New Haven
Chief of Police Anthony Campbell told me how ShotSpotters
controversial devices that listen for gunshots, integrate with
specialized software so when a gun is fired, nearby swivel cameras
instantly alter their direction
to the location of the weapons discharge.
Officers can also use software to lock building doors from a control
center, and companies are developing analytics to alert security if one
car is being followed by another
Toward a “Minority Report” World
Video analytics captures a wide variety of data about the areas covered
by smart camera networks. Not surprisingly, the information captured is
now being proposed for predictive policing: the use of data to predict
and police crime before it happens.
In 2002, the dystopian film “Minority Report/”/ depicted a society using
“pre-crime” analytics for police to intervene in lawbreaking before it
occurs. In the end, the officers in charge tried to manipulate the
system for their own interests.
A real-world version of “Minority Report” is emerging
through real-time crime centers used to analyze crime patterns for
police. In these centers, law enforcement agencies ingest information
from sources like social media networks, data brokers, public databases,
criminal records, and ShotSpotters. Weather data is even included for
its impact on crime (because “bad guys don’t like to get wet”).
In a 2018 document
the data storage firm Western Digital and the consultancy Accenture
predicted mass smart camera networks would be deployed “across three
tiers of maturity.” This multi-stage adoption, they contended, would
“allow society” to gradually abandon “concerns about privacy” and
instead “accept and advocate” for mass police and government
surveillance in the interest of “public safety.”
Tier 1 encompasses the present where police use CCTV networks to
investigate crimes after-the-fact.
By 2025, society will reach Tier 2 as municipalities transform into
“smart” cities, the document said. Businesses and public institutions,
like schools and hospitals, will plug camera feeds into government and
law enforcement agencies to inform centralized, AI-enabled analytics
Tier 3, the most predictive-oriented surveillance system, will arrive by
2035. Some residents will voluntarily donate their camera feeds, while
others will be “encouraged to do so by tax-break incentives or nominal
compensation.” A “public safety ecosystem” will centralize data “pulled
from disparate databases such as social media, driver’s licenses, police
databases, and dark data.” An AI-enabled analytics unit will let police
assess “anomalies in real time and interrupt a crime before it is
That is to say, to catch pre-crime.
Rise of the Video Surveillance Industrial Complex
While CCTV surveillance began as a simple tool for criminal justice, it
has grown into a multibillion-dollar industry that covers multiple
industry verticals. From policing and smart cities to schools, health
care facilities, and retail, society is moving toward near-complete
visual surveillance of commercial and urban spaces.
Denmark-based Milestone Systems, a top VMS provider with half its
revenues in the U.S., had less than 10 employees in 1999. Today they are
a major corporation that claims offices in over 20 countries.
Axis Communications used to be a network printer outfit. They have since
become a leading camera provider pushing over $1 billion in sales per year.
BriefCam began as a university project. Now it is among the world’s top
video analytics providers, with clients, it says, spanning over 40
Over the past six years, Canon purchased all three, giving the imaging
conglomerate ownership of industry giants in video management software,
CCTV cameras, and video analytics. Motorola recently acquired a top VMS
provider, Avigilon, for $1 billion. In turn, Avigilon and other large
firms have purchased their own companies.
The public is paying for their own high-tech surveillance three
Familiar big tech giants are also in on the action. Lieutenant Patrick
O’Donnell of the Chicago police force told me his department is working
on a non-disclosure agreement with Google for a video analytics pilot
project to detect people reacting to gunfire, and if they are in the
prone position, so the police can receive real-time alerts. (Google did
not respond to a request for comment.)
Video monitoring networks inevitably entangle and implicate a whole
ecosystem of vendors, some of whom have offered, or may yet offer,
services specifically targeted at such systems. Microsoft, Amazon, IBM,
Comcast, Verizon, and Cisco are among those enabling the networks with
technologies like cloud services, broadband connectivity, or video
In the public sector, the National Institute of Standards and Technology
“public analytics” and communications networks like the First Responder
Network Authority, or FirstNet,
real-time video and other surveillance technologies. FirstNet will cost
$46.5 billion, and is being built by AT&T.
Voxel51 is another NIST-backed venture. The public is thus paying for
their own high-tech surveillance three times over: first, through taxes
for university research; second, through grant money for the formation
of a for-profit startup (Voxel51); and third, through the purchase of
Voxel51’s services by city police departments using public funds.
With the private and public sector looking to expand the presence of
cameras, video surveillance has become a new cash cow. As Corso put it,
“there will be something like 45 billion cameras in the world within a
few decades. That’s a lot of (video) pixels. For the most part, most of
those pixels go unused.” Corso’s estimate mirrors a 2017 forecast
from New York venture capital firm LDV, which believes smartphones will
evolve to have even more cameras than they do today, contributing to the
Companies that began with markets for police and security are now
diversifying their offerings to the commercial sector. BriefCam,
Milestone, and Axis advertise the use of video analytics for retailers,
where they can monitor foot traffic, queue length, shopping patterns,
floor layouts, and conduct A/B testing
Voxel51 has an option built for the fashion industry and plans to expand
across industry verticals. Motionloft <https://motionloft.com/> offers
analytics for smart cities, retailers, commercial real estate, and
entertainment venues. Other examples abound.
Public and private sector actors are pressing for a world full of smart
video surveillance. Peleg, for example, told me of a use case for smart
cities: If you drive into the city, you could “just park and go home”
without using a parking meter. The city would send a bill to your house
at the end of the month. “Of course, you lose your privacy,” he added.
“The question is, do you really care about Big Brother knows where you
are, what you do, etc.? Some people may not like it.”
How to Rein in Smart Surveillance
Those who do not like new forms of Big Brother surveillance are
presently fixated on facial recognition. Yet they have largely ignored
the shift to smart camera networks — and the industrial complex driving it.
Thousands of cameras are now set to scrutinize our every move, informing
city authorities whether we are walking, running, riding a bike, or
doing anything “suspicious.” With video analytics, artificial
intelligence is used to identify our sex, age, and type of clothes, and
could potentially be used to categorize us by race or religious attire.
Such surveillance could have a severe chilling effect on our freedom of
expression and association. Is this the world we want to live in?
The capacity to track individuals across smart CCTV networks can be used
to target marginalized communities. The detection of “loitering” or
“shoplifting” by cameras concentrated in poor neighborhoods may deepen
racial bias in policing practices.
This kind of racial discrimination is already happening
in South Africa, where “unusual behavior detection” has been deployed by
smart camera networks for several years.
In the United States, smart camera networks are just emerging, and there
is little information or transparency about their use. Nevertheless, we
know surveillance has been used throughout history to target oppressed
groups. In recent years, the New York Police Department secretly spied
on Muslims, the FBI used surveillance aircraft to monitor Black Lives
Matter protesters, and the U.S. Customs and Border Protection began
building a high-tech video surveillance “smart border
across the Tohono O’odham reservation in Arizona.
Law enforcement agencies claim smart camera networks will reduce crime,
but at what cost? If a camera could be put in every room in every house,
domestic violence might go down. We could add automated “filters” that
only record when a loud noise is detected, or when someone grabs a
knife. Should police put smart cameras inside every living room?
The commercial sector is likewise rationalizing the advance of
surveillance capitalism into the physical domain. Retailers, employers,
and investors want to put us all under smart video surveillance so they
can manage us with visual “intelligence.”
When asked about privacy, several major police departments told me they
have the right to see and record everything you do as soon as you leave
your home. Retailers, in turn, won’t even approach public disclosure:
They are keeping their video analytics practices secret
In the United States, there is generally no “reasonable expectation” of
privacy in public. The Fourth Amendment encompasses the home and a few
public areas we “reasonably” expect to be private, such as a phone
booth. Almost everything else — our streets, our stores, our schools —
is fair game.
Even if rules are updated to restrict the /use/ of video surveillance,
we cannot guarantee those rules will remain in place. With thousands of
high-res cameras networked together, a dystopian surveillance state is a
mouse click away. By installing cameras everywhere, we are opening a
To address the privacy threats of smart camera networks, legislators
should ban plug-in surveillance networks and restrict the scope of
networked CCTVs beyond the premise of a single site. They should also
limit the density of camera and sensor coverage in public. These
measures would block the capacity to track people across wide areas and
prevent the phenomenon of constantly being watched.
The government should also ban video surveillance analytics in publicly
accessible spaces, perhaps with exceptions for rare cases such as the
detection of bodies on train tracks. Such a ban would disincentivize
mass camera deployments because video analytics is needed to analyze
large volumes of footage. Courts should urgently reconsider
the scope of the Fourth Amendment and expand our right to privacy in
Police departments, vendors, and researchers need to disclose and
publicize their projects, and engage with academics, journalists, and
It is clear we have a crisis in the works. We need to move beyond the
limited conversation of facial recognition and address the broader world
of video surveillance, before it is too late.
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