US Government Agency Highlights Analytics Failure

By: Brian Karas, Published on Jul 15, 2016

Count the US government among those not satisfied with video analytics.

A US government agency held a workshop on video analytics for public safety. We spoke with Conference Chair John Garofolo [link no longer available] to understand what was discussed at the event and what government and public safety officials are looking for in video analytics.

In this report, we share their concerns, what they are looking for and how commercial analytics providers are still far from truly meeting that.

***** *** ** ********** among ***** *** ********* with ***** *********.

* ** ********** ****** held ********* ** ***** ********* for ****** ******. ** ***** **** ********** Chair John ******** [**** ** longer *********] ** ********** **** was ********* ** *** event *** **** ********** and ****** ****** ********* are ******* *** ** video *********.

** **** ******, ** share ***** ********, **** they *** ******* *** and *** ********** ********* providers *** ***** *** from ***** ******* ****.

[***************]

What *** ** **********/****** ****** *****

***** ********* ** ****** Safety Conference ***** **** **** are ******* *** ****-**** alerts ******:

  • ************* ******** ********* - fighting, ****** ******* ** water, ****** ******* ** train/subway ******
  • ***-*****/******* ***** ********* - detecting ***** **** ****** or **** *********
  • ********** ****** - ********* **** * ******* series ** ****** ** similar ** * ******** series ** ******

**** **** ********** **** common ******** ***** ******** by *** ******** ********, like *********, ** ********* *** not ************ ****** *** their *****.

 

 

Security ************* *** **** ** *******

**** ***** ***** ********* were **** ** ********** leaders ** ****** ****** analytics, *** ********** ***** ********* GE *** *** ************. This *** **** ********** at *****, *** *** complex ************ ** ********* "sophisticated ******" ** ****** safety, ******* ********** **** of ******* ****** ******** what *** ******** ****** "Deep ********".  

*** ******* ** **** traditional ******** ********* ********* are *** ******* ** simple ***** *********, *** not ******* ** ***** understanding *** ****** ***** and *** ******* *** moving ** *********** ** that *****. 

How ***** ********* *** **** *****

******** ********* ** * common selling ***** ** ********* systems *****. ********* ********* ***** ** acting ** * ***** burglar *****, **** ************ like ********* *** ****** regions, ******** ********* ** reduce *** ****** ** on-site ******, ** *** remote ****** **** ***********.

*** ******** ********* ******* is ******** ** ****** safety, *** *** ********** event ********* **** *** address ****** ****** ************ enough ** ** ** value. 

Limited ******* ****** ** ** ****

***** ***** *** * few ******** ***** ******** developers ********** ** ** what **** ** ******* for, *** ** **** have **** ******** / question *****:

  • ***** ****- ****** ***** **** $100 ******* ** *******, multiple ****** ** *******, pivots **** ******** ** non-security *** **** **** to *******, *** ******* rebranding **** *** ***** the **** ** ********* and *********** **** * standards ************ ***** ******
  • *******- ******* ****** **** child *********, ******* ****** widescale *** *** ****** ****** safety ***********
  • ***- ******* ***********, ******* stability ****** ****** *** company, ** ***** ***** on *********

Big *** ***** ******

**** *** ** ********** really ***** *** **** ********** providers *** ********** ***** has * *** ***.  The ****** ***** *** been ** ***** ** intelligent ******, ***** *** limited ***** *** ****** safety ************.  ********* ********* still ******** ** ******* systems **** *** ****** false ****** ******* **** missing ******** ****** ******** in ******* **********.

 

 

 

 

 

Comments (8)

What they are asking for sounds like the sort of solutions that are most likely to be provided by those who lead in deep learning research, large companies like Google, Microsoft perhaps... and hopefully they'll make their solutions available to the rest of us. Nvidia has some great new GPU hardware that would be ideal for incorporating modern machine learning solutions into VMSs, but I think they may need to come down in price a bit. Another thing that needs to happen is for the US government not to hamper the industry with their patent system, but the chances of getting the US government to understand their incompetence in this area zero, zip, zilch...

What they are asking for sounds like the sort of solutions that are most likely to be provided by those who lead in deep learning research

Yes, I completely agree. One thing I have said multiple times over the years is that "video analytics" is too generic of a terms to apply to as many things as we do.

I think it would be beneficial for the security industry to start defining video analytics sub-functions with words or terms that better describe how the system works or what it does. Similar to how we can describe cameras in terms of resolution or physical interface, etc.

The other thing I note, is that just by "asking" for these features, sort of implies that they have an expectation that the industry can deliver, or will be able to the not-too-distant future. That is they are aware of the significant breakthroughs that have happened in machine learning recently. If they had asked for the same solutions 15 years ago, they may have made them selves look silly.

If they had asked for the same solutions 15 years ago, they may have made them selves look silly.

10 years ago at 3VR, I talked with a number of government / public safety people who wanted these types of things and even more wild.

For example, I had an intelligence agency person ask if we could alert if a guy boards a train with a package at one station and exits at another station without the package. He was seriously unimpressed when I said no.

That is they are aware of the significant breakthroughs that have happened in machine learning recently.

I think the simpler explanation is that those are simply their needs.

It sounds like there are a few tiers structured thusly:

  • VMD - VMS or camera side motion detection. Used to be considered analytics but should really be stricken from the list.
  • Basic Analytics - Crossed line detection, dropped object, PTZ tracking, etc.
  • Moderate Analytics - Avigilon/AgentVI/Bosch IVA/etc which can be run camera side and classify objects. Basic LPR (e.g. OCR) would likely fall here.
  • Advanced Analytics - Advanced LPR, retail analytics, facial recognition, etc.
  • Mythological Analytics - All the analytics that have been mentioned as of late which seem far-fetched. Examples include: Age/gender classification, predictive behavior recognition, noise analytics, etc.
  • Deep learning/cloud analytics - the particularly complex, processing heavy analytics which are still in their infancy.

Is that accurate?

I think it is a thoughtful segmentation, but I do not think it is accurate.

For example, in "Basic Analytics", there is no way "dropped object" should fall in that category. In fact, non-human/vehicle object detection should be a unique category (IMO).

I was thinking something more like:

Perimeter Protection - Classify a human, and ignore non-human objects, in a "sterile zone" style application. This implies something that is reasonably good at picking up people, and ignoring other objects, but is not concerned with things like crowd detection, or accurately classifying a group of people.

Business Intelligence - Analytics that focus on counting and path tracking. Should be able to reasonably count a large group accurately, but less concerned with object rejection (for their standard applications you would be unlikely to have to deal with animals or other common false-alarm objects).

Behavior and Gesture Recognition - Can accurately track multiple persons in a scene, and identify basic gestures like waving arms, pushing, or actions like falling down, fighting/aggressive interaction, etc.

OCR - Anything that can read text or symbols in an image. This would include LPR functions, but also things like recognizing logo's or glyphs to alert on, or ignore.

This is just a short list, not meant to be comprehensive. Where the analytics run (camera, server, cloud) is a secondary classification, and while some functions might limit them to running only in a server or cloud environment today, they may be able to run within a camera in X years, so I would not make that part of the analytics definition.

Deep learning/cloud analytics - the particularly complex, processing heavy analytics which are still in their infancy.

Deep learning, i.e.neural networks, was in its infancy 20, 30 years ago, but seems to have come of age in recent years.

Deep Learning is the state of the art in object recognition/classification, but it is far from being the solution to *all* AI problems. It has become another bandwagon for researchers who are now proposing incremental work only.

Deep learning is still doing the lower level brain-like operations, but progress in higher level machine reasoning is just a bunch of heuristics and very little theory. I can see "200" different approaches for tracking and recognizing object on a video, but none of them have a formal theoretical framework.

BTW, my background is on signal/image processing and I remember stories from professors were army people will come to them seriously asking for mind reading technology.

Login to read this IPVM report.
Why do I need to log in?
IPVM conducts unique testing and research funded by member's payments enabling us to offer the most independent, accurate and in-depth information.

Related Reports

Rhombus Cameras, VMS and Analytics Tested on Nov 06, 2019
Rhombus boasts they have created "the new standard in Enterprise, cloud-managed video security" and told IPVM in January 2019 they offer twice the...
90+ Companies Profile Directory on Nov 06, 2019
While IPVM covers the largest companies in the industry regularly (like Axis, Dahua, Hikvision, etc.), IPVM strives to do a profile post on each...
nFlux AI Startup Profile on Oct 28, 2019
nFlux, an Amazon, Google, and Microsoft funded AI startup told IPVM their mission is to build the most intelligent video analytics platform in the...
RealNetworks SAFR Facial Recognition Profile on Sep 25, 2019
RealNetworks entered the surveillance market by giving away their analytics to schools for free, and is now targeting large commercial users with...
Hikvision Acusense Analytics Tested on Sep 23, 2019
Hikvision touts "The Magic Behind It All" in their new Acusense line are 'deep learning algorithms' inside these cameras and recorders. But how...
Directory of 70 Video Surveillance Startups on Sep 18, 2019
This directory provides a list of video surveillance startups to help you see and research what companies are new or not yet broadly known. 2019...
Vivotek "Neural Network-Powered Detection Engine" Analytics Tested on Sep 17, 2019
Vivotek has released "a neural network-powered detection engine", named Smart Motion Detection, claiming that "swaying vegetation, vehicles passing...
AI Video Surveillance (Finally) Goes Mainstream In 2020 on Sep 03, 2019
While video surveillance analytics has been promoted, hyped and lamented for nearly 20 years, next year, 2020, will be the year that it finally...
Scylla AI Video Analytics Company Profile on Aug 29, 2019
Scylla, an AI analytics startup, says they are targeting 1 Billion dollar valuation in 5 years and it "is not rocket science" to detect weapons and...
Anyvision Facial Recognition Tested on Aug 21, 2019
Anyvision is aiming for $1 billion in revenue by 2022, backed by $74 million in funding. But does their performance live up to the hype they have...

Most Recent Industry Reports

The Cowardly, Greedy "Leaders" of Video Surveillance - SIA on Nov 19, 2019
The video surveillance industry suffers from cowardly, greedy 'leaders' who are focused on maximizing easy money while undermining trust with the...
Hikvision Dual Lens Face Recognition Camera Tested on Nov 19, 2019
Hikvision's Dual Lens Facial Recognition camera, claims that it "adopts advanced deep learning algorithm and powerful GPU to realize instant face...
Top Manufacturers Gaining and Losing 2019 on Nov 18, 2019
2019 has been an explosive year for video surveillance, with the world's two largest manufacturers, Dahua and Hikvision, being sanctioned for human...
Hidden Camera Detectors Tested on Nov 18, 2019
Hidden cameras are a growing problem as cameras become smaller, cheaper and easier to access. However, some companies claim to be able to detect...
Wyze Fires Back at JCI - Your Patents Are Invalid, Pay All Of Our Costs on Nov 18, 2019
Goliath JCI targeted startup Wyze this summer alleging the fast-growing consumer startup was violating a slew of JCI's patents. Now, Wyze has...
ADT Stock Surges - "Leading The Commercial Space" on Nov 15, 2019
Don't call it comeback... but maybe call it a commercial provider. ADT, whose stock dropped by as much as 2/3rds since IPOing in 2018, has now...
Gatekeeper Security Company Profile - Detecting Faces Inside Vehicles on Nov 14, 2019
Border security is a common discussion in mainstream US news and politics, as is the use of banned Chinese equipment by US Government agencies....
Hikvision CEO And Vice-Chair Under PRC Government Investigation on Nov 14, 2019
In a surprising and globally covered move, Hikvision CEO Hu Yangzhong and Vice-Chairman Gong Hongjia are being investigated by China's securities...
Camera Field of View (FoV) Guide on Nov 13, 2019
Field of View (FoV) and Angle of View (AoV), are deceptively complex. At their most basic, they simply describe what the camera can "see" and seem...
UK Big Brother Watch: Hikvision Is 'Morally Bankrupt' on Nov 13, 2019
UK civil liberties advocate Big Brother Watch has condemned Hikvision as being 'morally bankrupt' following IPVM exposing Hikvision marketing...