Deep Learning Tutorial For Video Surveillance

Author: Brian Karas, Published on Oct 17, 2017

Deep learning is a growing buzzword within physical security and video surveillance.

But what is 'deep learning'?

In this tutorial, we explain deep learning specifically for video surveillance covering:

  • Traditional video analytic approaches
  • Machine learning vs deep learning
  • What makes learning 'deep'?
  • How deep learning can help analytics
  • The role of training
  • Examples of training for people and guns
  • Example of training for men vs women vs old vs young
  • Filtering alarms With deep learning
  • Training data not disclosed
  • Potential Problems Across Regions
  • Hardware Requirements
  • Evaluating Deep Learning Products

**** ******** ** * ******* ******** ****** ******** ******** *** video ************.

*** **** ** '**** ********'?

** **** ********, ** ******* **** ******** ************ *** ***** surveillance ********:

  • *********** ***** ******** **********
  • ******* ******** ** **** ********
  • **** ***** ******** '****'?
  • *** **** ******** *** **** *********
  • *** **** ** ********
  • ******** ** ******** *** ****** *** ****
  • ******* ** ******** *** *** ** ***** ** *** ** young
  • ********* ****** **** **** ********
  • ******** **** *** *********
  • ********* ******** ****** *******
  • ******** ************
  • ********** **** ******** ********

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

********

**** ******** *** ****** * *** ** ********* ** *** security ******** *** *** ********* ** ******* ******* ** *******-*** analytics ********. ** ** ********** ** ** *** ****** ********* of ********** ************ ** ******* ******** *** ** ***** ******* in * ******* ** ********** ** **** ********* ****** ********** and ******** ******* ****-***** ******** *** **********. *** *** ******** of **** ******, ** *** ********** **** ******** ************ ** the ******* ** ***** ************, ****** ********* *****.

Traditional ***** ********* **********

***** ** **** ******** *** ** *********, ******* ********* ****** on **** ***** *********** ** **** ******* ** ****** (*.*.: people *** * ******** ****** *****, ******** *** * **** aspect *****), *** ****** *********** ** **** ******. *** *********** step ***** ****** ***** *** ****** *** ******* ****, ** that *** ****** ***** **** ************* ****** ******* ***** *** ground (*****, ******, ***** ******). *********** **** ****** ****** **** filters, ***** ** **** ******* ***** ** ******* ** **** were *** ***** ** *** ***** *** ************ ** * given ****.

***** ********* *** ***** ****** **** *** ** *** ***** place ** *** *****, ** *** ***** ****, *** ********* motion ***** ** ******** ** ********* ** ** *** ** object ** ********. ** **** *****, ******* **** ****** ***** or ***** ********* ***** ** **** ** ********* ** ** object *** * ******, ** *******, *** *******. *******, ***** systems ********* *** *** **** * *** **** * ***** or * ********** **** * *******, ** **** *** *** doing **** ******** ** ****** ******* *** ******* ******* **** at ****** ***********.

***** ******* *** **** ***** ** ****************** **** ******* ** not **** *** ***-*** ************, * ****** ******** ** *** ground ******* ****** ******* ******** ***** ** ********** ** * vehicle ******* ** * ******, ***** ***** ****** ***** *** uniform ********:

**** ******** ** ********* *** *********** ******* ***** ** ***-********* static ******** ** ********* ******** ** ** ******* ********.

Machine ******** ** **** ********

******* ******** *** **** ******** *** *******, *** **** ******** different ********** ** * *******. ******* ******** **** ***-********** ************ to ***** * ******** ** ********* ** ***** ** ** object, **** ******** ******* ***** ****** *** *************.

******* ******** *** ** ****** *** ** ********* * ***** walking ** *********** ********** **** ** *** ***** ** *** height ****** ** ****** **** *** *****, ***** ****** ** movement ** **** *** **** **** ** **********, ** ****** move ** * *********** ********* ******* ** ********, ** ****** have **** ***** *** ******* ******* (************ ********), *** ** forth. **** *** ********* ** **** *** *****, ** **** look *** ***** **********, *** ** ** ***** ****** ** them, ** **** ****** *** ***** ******** * ****** *******.

** **** ********, *** ******** ** *** ****, *** **** the **** ********** *********, **** ** * *****. ** **** breaks *** **** **** **** ******* **********, *** ***** *** similarities ****** *** (** ****) ** *** **** **** ** can *** ** ***** ** ************* ** *** ** ********* future ********* ** *** **** *******.

*** **** ******** ********* ****** **** ** **** ******** ** its *** **** ** **** ******* ** **** *** ******* learning ********* *** ******** ********** ****. ** **** *****, *** deep ******** ********* **** ** **** *******, ******** ****** **** humans *** *** **** ******* ** ******** ******, ** **** would **** **** **** **** ********* ** ******** ******, **** as **** *** ******** ************* ** ****** *************** ** ******.

What ***** ** "****"?

**** ******** ***** **** *** ****** **** ******* * ****** of ************ ************** ******, *********** *******, ** ***** *********. * system ******* ** ******** ******** ** ***** ***** **** ******* to ***** ** ****** ******** **** **********, **********, ******** ** badges ** *******, ****** ******, *** **** *****.

**** *** ****** ** ********, **** ****** ***** **** ******* them ******* ******** ****** (*********** ** *** ****** **** ** the ***** *****) ******* *** ******** ** *** ******* ***** various ******* *************, *** ****** * ******** ** ***** ***** of ******* *** **** ****** ** *** *****. ** **** case, "********" *** *** ******* *******, ** ** ******* *** most ********, **** *** ****** ****** ********** *** ***** ******** elements ********** **** "********" *** "*******" ******** ** * ****** extent:

***********, **** * ******** ****** *** ******** *** *** ****** to ** ********** ** "****", ******* ** ** *** ******** for ***** ** ** **+ ****** ** ************** ** **** advanced *******.

*** ** *** ******** **** **** ******** *** ** ***********, the ***** ** *** ******** ******* ** *** * **** indicator ** ******* ****** *********** ** ***********. **** **** *** imager **** ** * ****** ** *** ** ******** *********** factor ** ********** ** ***** *******.

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

**** ******** *** * ****** ************* ** *** *************** **** define ******* *******, ******* ** ******* ** ************ ** ***** appearance. **** ***** ** ****** **** ** ******** ******* ********** in *********** **********, ** **** *** ****** **** *** ***** any ***** *********** ** ************.

********* ************ ********* ******* ****** ********* ** ****** ** * time ** ***** ***** ** ****** *** ** (*.*.: *********** detection *****-*****). ** **** *****, ********* *** **** ** ****** "abnormal" ********, ****** ******* **** **** ******* ****, ** * large ***** *** ****** ** *** **** ********* ******** (**** as ** * ***** **********). ***** **** **** ***** ****** is ** *** **** ***** *****, ********** ******* **** * blob ** ****** ** * *****, *** *** * *** (or * *****, ** * ****). ** ***** **** ** better ********* ******* ** ******** ** * *****, **** ******** helps ***** ********* ******** ***** ******** *** **********.

The **** ** ********

* **** ****** *******'* *********** ** ***** ** *** ******* and ********* ** ****** **** *** ********. ******* * ****** that *** ******* ** ********* ****** **** ** ******* ** images ** **** ********, ** **********. **** ****** ***** ****** fail ** ********* * ******** ** ******** ******, ******* *** training **** *** *** ****** ******. ** ****, ** ***** perform ***** **** * ******** ********** ****** ******* ******** ******.

*******, **** *** ******** ** ***** ***** **** **** *********, or **** ** ********* ** *************** ** *** ******* ******** for **************. ***** ******** ****** ****** ******* ***** ** *** object **** ******** ******, *** *** ***** **** ******, ** a ******* ** ***** *** ********.

******* * ********** ** ******, *** ******* **** **** * category *** ** **** **** *********.********** *** ******** ********* **** *** *** ********, ** ** contains ******** ** ****** *********** **** ********* ** ********** *** sub-categories. ***"******" ************* ******** ****** *,*** ***-********** ** ******, **** *************** **** "warrior" ** "*********".

***-***** ***** ********* **** *** ********** ** **** **** *****-******* object *************** **** *******-******** *******. *** *******, * *** *** be *** ****** ** ********, ********** ** "**** ********* ******" and "********" (** **** ** ******** ***** *************** **** *****, shotgun, ***.).

**** **** ********, ** ***** ** ********** ** ****** *** system ** ******** ******** ************, ***** ** ***** ***************, ********* impractical *** *******-******** *******.

*********, ** ***** ***** * ****** ***** ****** ** *** and *****, ***** *** ***, ** ***** * *** ******* of *********** ****** *** ***. ********* ** **** **** *** becoming ******* ** ****** ************ ** ***** ********* ** ******* customer ******** ** ****** *** **** ** **** ************** ** browse ********.

Gender *** ***

*** ******* ***********, ********* ** ************ *****, *** ** ****** applications ** ** ****** ******** *********** ***** ** ***** ****** and ***. **** *** **** ******** ****, ****** ****** ** men *** *****, *** *** *****, **** ****** *** ******** to ****** ***** *********** **** ** ****** ** **** **** have **** ******* **. ******** ***** *******:

Filtering ****** **** **** ********

*** ********* **** ** ******* ****** ***** ************ ** ***** deep ******** ** * '******'. *** ******* ** **** ******** every ***** **** * ************ ****** ***** ** **** ******** intensive. ******* ** ***** ****, *** ****** ***** *** *********** video ********* ***** ** ****** **** *** ********* ******* ** analyze, **** ********** **** ****** ****** ******* ** **** ******** to ****** ** *** ****** ******* ** ********, *** *******, a ******, ******* ** * ***, * *********, * ****** or * *****. *** *******, ******* ********* **** ******** ******** ****, ** ***** **** ******* **** *********:

Training **** *** *********

**** * ********* ***********, ***** ** ** *** *** * user ** **** *** *** ****** *** *******, ************* ********* will *** ******* ******** **** *** ********. ***** ***** *** data **** ** ******** ** ********** ** *** ************ *** adapted *** ***-***** ** ***** *******, ********* ********* **** **** would **** ***** ************* ** ********* ** ***** **** ** re-use *** **** **** *** ***** *** ******** ********.

Potential ******** ****** *******

**** ** ******* ** ******** **** *** ** *********** **** systems *** ******* ***** ** ****-***** ******* **** *** ***** to *** ********** ******, *** ****** **** ***** *** ******* will ** ********. ******** *** **** **** ********* ** ***** vs. ****** ** ** * ******** ****** **. ** ********** area, *** ***** **** *** *** *** ****** ** *********** in ***** ****** ***** **** * ****** ** ******. ********, a ****** ***** ** ******* **** ********* ***** ** ******** or ******* ********. **** * ****** ******** ** ******* **** people ** ********* ********, ******, ***. ***** **** ******.

Hardware ************

**** ******** ******** ******** ********* *** *** *********** ********:

  • ******** *** ****** ******* **** ******
  • ********* *** ****** ******* ** * ******* **** * ****** or ********

**** ** ***** ******** **** ** *** *** ** ****, this ** ************ ********* ** *** **** ** *******/*********, ** they **** *** **** ************* ***** **** ****, ******* **** new ******** ** ********* ******** *** *** ********* *** ******** to **** **** **** * ******** *******.

******** ** **** *************** *********, *** ********* **** *********** **** designed *** **** **** **** *** **** **** ********, *** expensive, **** **** ** **** ** * ****** ** ********. Depending ** *** ****** *** ********** ** ****** **** *** training, **** ******* *** **** *****, **** ** ***** ** complete, *** ******* **** ** ******** **** ** ********. *** training ***** ******* * ***** **** *** *** *** ** use ** ******** *******, **** ***** ** ********* **** ***** relative ** *** **** ** *** ***** ****. ****** ** a ****** ******** ** **** *** **** ****-*********** ******** *****.

********* *** *** ** * ****** ** ******** **** * lower-power *** ** **** **** **** ******** ** **** *******. Here, *****-**** *** ** **** ******* **** *** ***** ***********, and *** ****** ** ******* **** *** ** ******** *** classified ** *** *****, ** *** **** ** ***** *** system ** ******** ** ******. ********* *********/********, ************** ******* *********** **** ******** *** ****-***** ************.********** *** ****-***** ********, *** **** ******** ***** ********* ***** designed *** **-***** ********.

Deep ******** ** ************ ********

******* ** **** ******** ***** ************ ******************* ********* ** *** ******** ******** ******** ******** **** **** level ** **** ******** ******** ** ************. ************ ***** **** facial *********** ** *********** ****** *******, ** *********** ****** ****** to ****** ******-**** ****** ************.

Evaluating **** ******** ********

** *** *** ********* ** ****** * **** ******** *******, IPVM ***** ******** ********* *** ******* ** ****** ** * location *** *********** ** ******* ** *** ******** ********** ** possible. ***** ************ ***** ***** **** ****-**** *********** *********, **** do *** ****** *** ****** ************ *** ****-***** ***********. *******, tests ***** ** ********* **** * ****** ** *-* *****, giving *** ****** ********** **** ** *** * ******* ** objects, ******** *********** ** ** ******** **** ***/***** ********** *** across * ****** **** *** **** * ***** *****-**** ******* lot ****.

Future ********* *** **** ********

********** ********, *** ***** ** *** ** *** **** ******** development ** ********** ** *** ***** **** ** ** ******* and ******** *****. *** ************ ********* ***** *** * ******* improvement, ** **** *****, **** ******** *******, *** *** ***** far **** *****. ************ *** ***** ***** **** ** ****, core ********, *** **** **** **** **** *** ******** **** we ********** *** ******** ********* ***** ** **** ****** ***** when ******** ** ***** *********** ** *** **** *-** *****.

***** **** ** ********* **** *** ***** ********* **** *** be ********* **** * **** ******** ******* *** ******* **** deploying * *********-********* ********. *******, ***** **** * **** ********* need, ************ ** ******** ***** **** ****** *** ******* *********, would ** **** ** ******** ******* ** ****** * ******** too ***** ** *** ********* ***** ** **** ********.

Comments (26)

Bob McCarvill

10/17/17 04:13pm

******** ** ****** ******, Deep ******** **** **** become * ****** *** bigger ****** ** ** try ** ********* ****** in *** *********. ******* will ** **** ** monitor ******** ***** *** also ***** **** ** event ******. ******* ********** can ******* ** **** used ** ****-******* ****.

Avatar

Nathan Wheeler

10/17/17 04:17pm

***** ********. ****** *** providing **.

Suresh Yendrapalli

Duranc | 10/17/17 07:18pm

*********** ******** *************** ******* vs **** ********.

Undisclosed Manufacturer #1

In reply to Suresh Yendrapalli | 10/17/17 08:08pm

*** **** * ***** with **** ******* ** the *******. **** *** article:

******* ******** **** ***-********** instructions ** ***** * computer ** ********* ** image ** ** ******, deep ******** ******* ***** things *** *************.

**** ** ** ****** like *** ******** ** machine ********. **** *********:

******* ******** ** * field ** ******** ******* that ***** ********* *** ability ** ***** ******* being ********** **********.

********* ** ** *************, Deep ******** ** **** a ******* **** ** machine ********, *** ** many ********** **** ***** automatically ** ********** ******** data. *** ************** ************* * **** ** machine ******** **** **** requires **** ** ******** data, *** ***'* ********** deep ********. * **** used **** ********** *** detecting ******* ****** ** a *****, **** ******* thousands ** ****** ** license ******.

Avatar

Brian Karas

IPVM | In reply to Undisclosed Manufacturer #1 | 10/17/17 08:35pm

************ ******* ** ******* Learning******* **** ** ******* learning ************ ** ************* data **** ** ***** analytics, ***** ** *** context ** **** ******.

********** ******* ** **/******* Learning/Deep ********** **** ********-****** *******. They *** *** ********* example ** **** ***** machine ******** ******** ****** products ***** ******** **** "hand ******" ** ***** to **** *** ******** process **** *********. **** hand ****** ** *********** helping ****** ******* **** in *** *** ** consideration *** ******** ** get ****** ******* *******.

** ** ****** ***, one ** *** **** best *********** ***** *** machine ******** *** **** years *********** ******, ****** ** ***** required * ***** **** of ****-****** ** *** the *** ****.People ***** ** ** *** ***** ****-***** *********** **** **** ********* ******* ** *** ******* ***** ******** ***** ** ****** ******* *** *******; ***** ********* ** ********* ** ** *** ***** *****; * ********** ** ********* *** ******* “*-*-*-*.” From all those hand-coded classifiers they would develop algorithms to make sense of the image and “learn” to determine whether it was a stop sign. [Emphasis IPVM]

**** ** ******* ** my ******* ** *** report ** ****** *** machine ******** ****** **** coarse ********** ** ****** that ** *** *** to ********* **** ******* in *** ***** ****** be ******* ********.

Undisclosed Manufacturer #1

In reply to Brian Karas | 10/17/17 09:02pm

*** **** **** *** nvidia *******

******* ********** *** **** ***** is *** ******** ** using ********** ** ***** data, ***** **** **, and **** **** * determination ** ********** ***** something ** *** *****. So ****** **** ****-****** software ******** **** * specific *** ** ************ to ********** * ********** task, *** ******* ** “trained” ***** ***** ******* of **** *** ********** that **** ** *** ability ** ***** *** to ******* *** ****.

* ********* *** ********* you ****** ******** ***********. I ** *** *** it ** * ********** of ******* ********. * see ** ** ** example ***** ******* ******** only ***** *** ** far, *** **** ** some ***** ******** **** coded ************ ** ******* the ********. ** **** were * ********** ** machine ********, **** ** would ** ********* ***** methods ** ********* ********, such ** *** **** Cascade ********* **** ** see ********, *** *******.

Igor Falomkin

AxxonSoft | In reply to Undisclosed Manufacturer #1 | 10/18/17 07:13am

** ***** * ******** vision ******* *** *** to ****** * ************ model ** *** ********** to ** ********. *** example, ** ********* ******* plates ** **** ** define ************** **** * license ***** ***** ****. The **** ********** ******* machine ******** ** ********* computer ****** *** ********** neural ******** ** **** in ********* ******** ****** this ***** **** ** explicitly ******* ** * researcher *** **** ** may ** ******* ** machine ******** (*** *******, the **** ******** ********** may ** ***** ************* via ******* ********). ** artificial ****** ******** *** model ** ******* ********** via * ******** *** (and ** ******* ** even ***'* **** **** features *** ******* *** choosen ** ***** *** task).

** *** *** ********* to ****** * **** learning *******, **** ***** strongly ********* *** ******* be ****** ** * location *** *********** ** similar ** *** ******** deployment ** ********.

**** ** * **** good ******. ******* ** we ** *** ******* know ***** ******** *** used ** * ******* then **'* ********** ** predict ** ***** ********** the ******* **** **** pretty ****. *** ** ready **** *** ******* will **** ** ********** training ** **** **** exact ****.

Undisclosed Manufacturer #1

In reply to Igor Falomkin | 10/18/17 07:26pm

**** ***** **** ** explicitly ******* ** * researcher *** **** ** may ** ******* ** machine ********

***, *** * ***** argue **** **** ***** can ** **** ******* such **** ** ********, we ******** ********** ** not **** ** ****** mathematically **** * ******* plate ***** ****. *****, I **** *** ******* of ******* ******* **********. Feed ** **** ***** of ******* ****** ** be **** ** ******** data, *** ** **** learn **** ** ***** effort **** ** ** detect ******* ****** ** a *****.

Igor Falomkin

AxxonSoft | In reply to Undisclosed Manufacturer #1 | 10/19/17 05:33am

*** *** *****. * model *** ** **** or **** *******. *** both ******* *** ********** learning *** ******** **** other. * **** ****** to ********* **** ***** neural ******** ** *** to **** **** *** more ***** **** * researcher ** * ********. For *******, ** ******* classifier *** ***** ******** that *** ****** **** are (******) ******* ** a **********. ** ************* neural ******** **** ***** features *** ********** ****** learning *******. ** ****** there *** ***** * lot ** **** *** a ********** **** **** neural ******** :)

Undisclosed Manufacturer #1

In reply to Igor Falomkin | 10/19/17 07:55pm

***, ******. *** ** is *** **** * am *********** **** ***** on *** *********** ******* deep ******** *** **** traditional *******. ***** * disagree ** *** *** article ***** ** ****** the *********** ************ ** **** ***** traditional ******* *** ******** all ****** ********* **** learning. **** ** **** not *******, ** **** industry ** *** *****.

* ***** ** ** important ** *** *** terminology *****. *** *******, a ******* ****** ** entitled ** *** *** term******* ********** ***** ********* ********* without ** ***** ********* by *** ******* ** mean ********* **** **** it **.

Undisclosed Manufacturer #1

In reply to Undisclosed Manufacturer #1 | 10/19/17 10:18pm

* ******* ****** ** entitled ** *** *** term******* ********** ***** ********* ********* without ** ***** ********* by *** ******* ** mean ********* **** **** it **.

**** ** *****, **** this *************, **** ******* ********** their ******* ** ***** "machine ********" *** *****'* mention *** **** **** learning

...**** ******** ******** ****** and ******* ******** **** put **** * ...

*** ****'** ** ****** use **** ******** ** their *******.

Avatar

Skip Cusack

In reply to Undisclosed Manufacturer #1 | 10/18/17 11:21am

*****, ********* *******! * agree **** **** *****: Machine ******** ******* "********." The ******** ***'** ***** to **** ***** ** analytics ** **** * would **** "***** *****," because ***** ** ** learning. ***-******* ****** ***** are ****** ******* ** images ** *****. ****** there ** * ***-**-***-**** correcting ***** **** ** validating ******* ****, *** this ******** **** ** used ** ****** ** update *** ******** *****, I ***'* *** *** "machine ********" *** ** an ******** **********.

*******, ***** *** *** not ************ ******** *** use ** "********** ************."

Avatar

Brian Karas

IPVM | In reply to Skip Cusack | 10/18/17 11:35am

******* ********, ***** ******, however ** ** "******" by ****-****** **** ***** constraints ** * ******** in *** *******, ** as **** ****** **** the ******* ***** *******.

** ***'*, *** **** the ****** * ****** of ******, *********** ******* it **** **** **** all **** ** ****** ("these *** ******** ** people"), ** **** ****** the ************* *** *********** components ** *** ***, without ****** ** ***-****** anything.

Undisclosed Manufacturer #1

In reply to Brian Karas | 10/18/17 08:38pm

******* ********, ***** ******, however ** ** "******" by ****-****** **** ***** constraints ** * ******** in *** *******

*** ** ********, *** always ** *****. *** definition ** ******* ******** is ******* **** ****, you *** ********* *** limitations ** **** *********** machine ******** ********** **** the ***** ********** ** machine ******** *** ** doing ** *** ********* some ******* ******** ******* that ******* ** **** hand ****** ** ***********. It ***** ** **** accurate ** *** **** "traditional ******* ******** ********** versus **** ********", ****** than "******* ******** ****** deep ********".

**** ****** **** *** license ***** *******

* ***** *** **** the ************ ***** **** he ** ********* ** is *** *********** *** created ** *** ******** developers *** *** ******** an *** ******, *** a ******* ************ ***** created ** *** ***********/********* of *** ********** *** built **** *** ********* itself. ***** ****** *** be ******* ****** ** detect ******* ******, ** cars, ** ***** ********* only ** *** ******** data. *****, * **** my *********** **** *** OpenCV ******* **********, * got ** ** **** quite ****** **** *** and ** **** ***** constraints ** ************ ***** was ********.

Avatar

Brian Karas

IPVM | In reply to Undisclosed Manufacturer #1 | 10/18/17 09:16pm

***** *** ******** ******** ********* ******* ******** ** **** ********, where *** ******* ******** ****** ** ***** **** ****-***** ********** to **** ***, *** **** **** **** *********** ** ********** for *** ******** ******* ** ***** *** *********** ** *******.

**** ** *** *******:**** ******** **. ******* ******** – *** ********* *********** *** need ** ****!************ **** ****** ** ******* ** * ******* ******** ****** designed ** ****** ******* ** ******:

** ** ***** **** ** * ******* ******* ******** *******, we **** ****** ******** **** ** ** *** ****** *** whiskers ** ***, ** *** ****** *** **** & ** yes, **** ** **** *** *******. ** *****, ** **** define *** ****** ******** *** *** *** ****** ******** ***** features *** **** ********* ** *********** * ********** ******.

***, **** ******** ***** **** *** **** *****. **** ******** automatically ***** *** *** ******** ***** *** ********* *** **************, where ** ******* ******** ** *** ** ******** **** *** features.

**** ** ******* *******:

** ****’* *** ********** ******* ******* ******** *** **** ******** then? **** ******** ******************** ********, *** ***** * ******** ******* ******** ***** ***** need ** ** **** *** ** ****** **** ** ******** prediction (** ******* ** **** ****), * **** ******** ***** is **** ** ***** **** ** *** ***.

*** *******:

*** ********* **** **** ******** *** **** *********** ***** ** machine ******** ** **** ***** *** ****** **** ** ******* a ********** *** ** ******** ** ***** **** **** ***** predictions, **** ******** *** ******** *** ********** ******** ******.

*** *******, ** * ****** ****** ** ******** ***** ***** in * ***** ** ***** *** **** ** ** ***** be *** *** ********** ********, **** ** ***** *** ********. It ***** ******* ** *** ** ****** ***** **** ** can **** ** ********** *** ********* ******** ** ***** ** make ** *********** ********** ***** *** ******* ** *** ******.

*** *** ******* ** **** ******, ***** ** ***** *********/******* vision, ******* ******** ******* **** ***** **** *** ******* **** amount ** ****-****** ** ********** **** *** ****** **** ** determine **** ***-******* ** *** ***** ** ****** ******* *** learn ****. **** **** ** **** ****-******, **** ** *** need ** ** **** **** ****** **** ** **** ******** aspect ******, ** ******** ********** **** (** * **** ********** example).

**** **** ******* ******, ***** *** ********** ** ****, *** we ***** ***** **** **-***** ******* ** ***** **********, ***., but ** ***** ** ****** *** ***** ** * '********', and ******** **** *********** ** *********** *** **********.

Avatar

Skip Cusack

In reply to Brian Karas | 10/18/17 10:00pm

*****, *** *** ***** to **** * **** back **** *** ****. One **** **** **** this ********** ** **** it ******** *** ********'* loose *************, *** ***** vocabulary, ********* **** *** technologies. ****** * ****** more *********** ****** ** a **, ******* ** "Deep ********" *** ********* it ** *** **** greatest ***** ** ****** not **** **********. ** seems **** ***** ***** vendor ** *** & ASIS ** ******* **** learning, ** **, *** details *** **** ** exactly **** **** *** doing *** **** *********** how **** ** ****** works. *'* **********, *** cautiously **.

Undisclosed Manufacturer #1

In reply to Brian Karas | 10/18/17 10:01pm

** ******** ***** ** that ** ** ********** ******** ****** **** learning, ** **** ******** is * **** ** machine ********, ****** ** should ****** ******** ************************ *********** **** *** *********** you ******** (********* **** coded **********) *** *** implicit ** *** ********** of ******* ********. *** titles ** ***** ******** are **** ****** ** the *********.

**** **********, ***, ** you *** ******** ** entire *** ****** ***** on *********** ******* ******** techniques, *** **** ********** have **** ****** ** the ******** *** **** special ***** **** *** have ** ***** **** for (*.* *******, *** bars *** ***** ***** obscuring *******, ********* ***** backplates ***) ******* *** deep ******** ******** ***** to ***** ********** ** well **** *** *** replace ***** ******* ****** with **** * ****** stage. *** **** ** work ****** ****.

Undisclosed #3

In reply to Skip Cusack | 10/19/17 02:57pm

"*******, ***** *** *** not ************ ******** *** use ** '********** ************.'"

*****... *** ****'* ******* what ************** ** ***** **** Learning ******* *****...

Avatar

Brian Karas

IPVM | In reply to Undisclosed #3 | 10/19/17 03:12pm

**** **** ******* *.*. once, ** *** ********* and ***** ********** **** learning *****. **** ********** it ** * ******* of ********** ************, ***** is *** *****.

Undisclosed #3

In reply to Brian Karas | 10/19/17 04:00pm

"**** **** ******* *.*. once, ** *** *********..."

*** - '*****'.

** ******* *** ******* off ** ***** ****** in ***** ******** *** for******** *** ****. *** comedic ********* ***** **** everyone ********- ***** *** **** I *** **********.

Avatar

Brian Karas

IPVM | In reply to Undisclosed #3 | 10/19/17 05:36pm

********** ** ******* * guess. * ***** **** the ***** ******/******* ** the ***** *** "****":

Undisclosed #3

In reply to Brian Karas | 10/19/17 05:52pm

"****'* ******* **** ************** ** ***** **** Learning ******* *****..."

*** **** *********.

** ** ******* * Deep ******** ***** ** the *** ***** ****** - ***** ** *********** followed ** *** ********** Intelligence ****** (***** ***** to ****** *** * long ****).

*.*. ***** '**** ********' youtube ***** '*****' **** the **** ******** ********* by ****.

**** ** *** ** opinion ** ** ********* with, *******, ** ** simply **** **.

Avatar

Skip Cusack

10/18/17 11:48am

**** ****** ***** *********** to ** **** ** not ********, **'* ** initial *** ** ********** that *** **** ****** against ***** *** ********** (to ****** * ********** degree ** *********). ** these ******* ********** *** defined ** * ******, and ****** ******* ***** without *** ******** ** modification, ***** ** ** learning. ******* ********, ** any **** ** ********, needs ** **** * feedback (****** ******** ** negative) ** ****** *** learning.

Undisclosed Integrator #2

10/18/17 03:38pm

**** ** ***** * *** *** ** ********** *** ********** between *** *** ***'* **** ******** ** ***'*

*****://*****.**/-**********

David Nguyen

IPVMU Certified | 11/01/17 04:30pm

** *** **** ***** of ************** **** ********** in *****, *** ** was ********** ** ***** that ** **** *** concept ***** *** ** reality *** **** ******* in *** **** **** future.

Avatar

Jared Beagley

Seagate Technology | 11/01/17 06:59pm

**** ** * **** useful ******** ** ******* explain **** ** *** general ******** ** ******* learning, **** ********, *** AI. ** ******** ******** our ******* ************ ****** with ****** ****** ******* AI ** **'* **** intriguing ** ***** **** about *** ********* ** what **** *** *******.

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