UK Retailers Register FRT Video Feeds Directly With UK Police
The first step to the inevitable long term solution to the crime surge
UK Facial recognition and video analytics in CCTV meet with mixed feedback
Nice enables collaboration between UK
businesses/retailers and police
Israel-based enterprise software solutions provider
Nice
has recently partnered with the UK’s National Business Crime Centre (NBCC) to
assist businesses and police with investigations using its biometric
Digital Evidence
Management Software (DEMS).
&uuid=(email))
The move potentially paves the way for an improved way of intelligence-gathering
and more efficient policing through CCTV video feeds and other evidence
shared with law enforcement by retailers
and other businesses, but also raises fresh privacy issues related to the
collection of biometrics and other information by private organizations.
Participating
organizations can register their CCTV feeds with the NBCC through Nice
Investigate’s Public Portal,
allowing law enforcement officers to review footage, and potentially run
forensic facial recognition queries.
DEMS
can process not only face information from CCTV footage, but also other forms of
digital content, which can then be sifted through and analyzed using Microsoft
Azure.
A month after the beginning of the new partnership between Nice and the NBCC on
processing data collected by private businesses, we analyze its potential and
reflect coverage and opinions from the general public about it.
According to
ITPro,
pharmacy chain Boots
was one of the first large companies to share its CCTV data with DEMS,
with the company believing the move will help them “get
better at reporting crimes.”
Also talking to the technology publication,
Tony Porter,
Corsight AI’s CPO and former British Surveillance Camera Commissioner echoed
the point, explaining that he believes the
UK public would support
the broader use of the system if the appropriate protections are put in place.
Recent data from
NW Security Group
suggests the adoption of advanced video analytics will continue to grow in the
next few years.
At the top of this list
is facial recognition,
behavior or event-based analytics, ANPR, video motion detection (VMD), object
tracking, object detection and classification, directional detection, and OCR
(optical character recognition).
&uuid=(email))
Addressing privacy concerns
A separate study by
Enterprise Times
shows similar adoption rates of advanced video analytics, also highlighting how
the pandemic has brought about a new series of crowd tracking and prediction
systems.
&uuid=(email))
The
same survey, however, suggests one of the challenges for surveillance system
operators remains to
balance privacy with
protecting business and the public.
For instance, the data
showed that the video shaping feature used by
Amazon Ring cameras still captured the whole field of view, with unfiltered
images potentially still being extracted.
Collection and storage of data captured by surveillance is also a recurrent
issue, the survey suggested, with very little signage in large cities having
adequate and up-to-date information on it.
The industry’s view
A recent
post on
IPVM (subscription
required and recommended), a video surveillance industry website, has raised
issues connected to identifying known thieves in-store using facial recognition.
“Why shouldn’t they be allowed to use facial recognition to identify a known
thief in their store?” asked one reader in kicking off the conversation. “So
you’re also giving a thief the right to privacy so they can rob you?”
IPVM founder John Honovich replied to the post by asking additional questions
related to what retailers should do if they get a match.
“Can they be certain the match is correct? And if you are certain, what are you
going to do? Having the store manager apprehend a criminal? Call the police and
hope they come in time?”
Additionally, Honovich explained that while London Police
have long grappled with the risks of apprehending suspects, facial
recognition makes this even more difficult.
“Some proponents of facial recognition in retailers had advocated detecting all
sorts of criminals in stores (e.g., a child molester walks into Walmart and an
alert is generated),” Honovich explained.
“For [Organized Retail
Crime] or shoplifting when the person may actually commit a crime then and
there, I see the potential.
For general criminal alerts, this strikes me as high risk (‘Excuse me sir but,
no offense, my computer system says you look like a child molester’),” he added.
While there is no consensus over these issues, it is interesting to look at the
debate, and how it continues to evolve as facial recognition solutions become
more and more accurate.
biometricupdate.com
&uuid=(email))
&uuid=(email))
