Video Analytics 101

By: IPVM Team, Published on Mar 16, 2020

This guide teaches the fundamentals of video surveillance analytics.

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

  • *** *** ***** *********
  • ***** ********* *******
  • ***** *** ********* *********? Camera? ******? *****?
  • ******** ***-***** *********
  • *** ** *********? ********* and **************
  • ****** ** ** *********
  • ***** ** *******
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  • ** ****** ******** *********
  • ** ************** ******
  • **** ******** ** *** Ongoing ** *******
  • ******** *********** ** *******
  • *** ******** ********* ** Servers *** *********
  • **** ******** / ** Mainstream ** ****
  • ****** ****** **. **** Systems
  • ***** ******** ******** ********

Why *** ***** *********

************, ************ ****** ** a ****** ** ******* multiple ******* ************** *** visually ******* **** *** occurring ** **** ** order ** ****** ******. However, **** ** *********** and ***** ** ****** because ****** *** **** monitor ** **** ***** at * ****, ******* to ****** ******.

***** ********* ******* ****** streams ** ****** ******** events, **** ** ****** or ******** ******, ******* faces, ** ******* ******. These ****** *** **** be **** ** ******* recording ** ****** ** operator ** **** *** effectively ******* **** ******* at *** ****.

Video ********* *******

****** ****** ** **** specific *******, ** **** to **** *** ******* clear: ***** ********* *********** varies***********, ******* **** **** strong ** ********. ***** should *** ***** ************ marketing ** *********, ** it *** ************ **** many *************** ****** *** overstated ***********.

***** *** **** ********** analytics *********, **** **** new ******** *** ************ in *** **** *** years ***** **** ******* drive ***********, *** ***** are **********, *** *** rule.

Historic ******: *** (***** ****** *********)

***** ****** ********* (***) is *** ******** ***** used ** ******* ** try ** ****** ********. It ** *** ** nor '*****' *** **** may ****** ** ** such.

*** ******* ** ********* rather ********** ******* ** pixels **** *** ***** to *** ****. ** enough ****** ****** ** enough ** * *****, the ****** ******** ****** detected.

*** ***** ***** ***** how *** ***** '***' a *** ****** *** correctly ********* ******:

VMD ***** ** ***** ******

*******, *** ** *** intelligent ****** ** **** whether ****** *** ***** movement ** ***, **** that **** *** ******. Because ** ****, ** makes **** ******** *** things **** *******, ******, branches, *******, *** ****** may ******* **. *** example, *** ***** ***** shows *** ****** * mistake ******* ** **** not '****' ******* *** changing ****** *** * person ** * ******:

\

Basic ********* **** *******

** *** ** ****** false ******, ************* ********* filters ***** ********* ** remove ***** ****** ******, leading ** ***** ***** analytics. ***** ******* *** include ****** **** ******/***** ratios, ****** ******, ********** motion, ***. **** ******** performance ******** ** ****** VMD, *** ***** ****** were/are ***** **** ******, with *******, ******, ****, animals, *** ***** ****** triggering **** ********* (*** our****** ********* ********).

Why ** *********

******* ** *** *********** to ******** ***-***** ********* and *** **** ***** alerts *** ***, ** analytics **** ********* *** video ************ ** ******** detection ******** *** ***** significantly **** ************** ************.

*** **** *********** ********* of ** ********* ** the ****** ** ******** effectively ****** *** **** detected ******: '** **** a ******?', '** **** a ***?', '** **** a *******?', '** **** a ***?' ***.

Basics ** ** *********

** ********* ******* **** accurate *********** ** ******** 'training' ** ***** **********. Simply ***, ***** ******* of ***** ******* *** analyzed ** ***** **********, which ******* ***** ******** determine ******* *** ****** is **** ** ** labeled **.

** **** * ****** learn ** ****** *****, many **** ****** *** fed **** *** ** system ** ** *** analyze *** ***** **** a **** ***** ****:

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**** ******** ** ********* automatically, ******* ** ******** defining *** ** ***** aspects ** *******. *** algorithm '******' ** *** own, ******** ** ****** ********.

AI ****** *********

*** **** ***** ***** of ** ** *********** if *** ****** ** looking ** * **** object ** ***** (****, dust, ******, *********). ***** more ******** **** ****** VMD, ** ** ***** challenged ** ************* ******* (rain, ****) *** ******** changes:

*** **** ***** ** AI ** *********** ** the ******** ****** ** a ****** ** ***. Detecting ******** ******* (******/*******/*******) is **** *********** **** simple *********, *** ******** higher-level *******.

**** **** *********** ** determining ** *** ****** is * ******, *******, or ***** ****** ** not.

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*******, ****** *** ******** are *** **** ****** while ***** ******* **** to **** ** ********* in **** ******** *** expensive *******.

AI ****** **************

***** ********* *** ** trained ** ****** ******** types ** *******:

  • ****** - ***, ******, ethnicity, ******** *****
  • ******** - ****, **** (car ** *****), *****, direction ** ******
  • ******* - **** (*** vs ***), *****
  • ********* ******* (****, ****) - ****, ****** (****-******), type (****** ** *****)

Person **************

******* * ******'* ******** attributes *** ******** *** finding * ******** ****** during ** ***** ** incident, ** ** ******* to ******** ******, ***, clothing **** *** *****, glasses, ****** ****, ***:

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*******, **** ** ***** classifications *** ********* ***********, and ** **** ******* of ******** *****, **** person ************** ********* *** not ****** ********.

Vehicle **************

* *******'* ******** ********** are *** **** ****** sources ** ***** *********** about ** ********, ** when ******* *** * person. ** *** ** trained ** ******** *** vehicle ****, *****, ****, model *** ********* ** travel:

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*******, ** ****, **** and ***** *** ********* challenging, ****, *** ********* marketed ** *********** ***/**** manufacturers.

Animal **************

**** **** ****** **** person ** ******* *********, AI *** ** ******* to ******** *** **** (dog, ***), *** *****.

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Inanimate ****** **************

********* ********** ******* *** help ******** ******** **** to *********** ********* **********. AI *** ** ******* to ******** *********, *****, briefcases, ****, *** ******** specific *************** ** ****:

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*******, ** ****, ****** detection *** ************** *** guns *** ********* ******* from *********** *********, ***** can ** ********* *** come **** *********** **********. Classification ** ********* *** other ********* ******* (**********, packages, ***.) *** ** very *********** ** ****, based ** ********, ****** and ********.

Color *********

***** *** ** ** important ****** *** *********** an ****** **** ********* for * ******** ****** or *******. ***** ********* can ** ******* ** themselves ** ** *********** with ***** ******* (********, vehicle *****):

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*******, ***** ********* *** perform ************ ** *** light ** *&* ** night *****, ********* ****** as ****** ** ***** and ****:

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******

********* *** ** ******* to ****** ******** ***** of *********. *** **** common ******** ********** ***:

  • ******** / **** ******** and *********
  • *********
  • ****** ********
  • ****** **** ****** / Removed

Tripwire / **** ******** *** *********

********* **** * ****** enters ** ********** * restricted **** *** ** critical ** ****-******** *********, or *** ****** *** safety ********.

**** ** *** **** basic ******** ******** *** is ******* ** **** cameras:

********* ** **** *** the **** ******* ** a ******** ********, *** the ***** ** ********* by * ****** ******** an ****, ****** **** just ******** * ****.

*********

******* * ****** ******** or *********** ** **** is *** ****** ****** to ******* ** *****, if **** *** ***** detected **** ******* ****** an ********** *****, ********* alerts *** *******.

******* ********* ********* ********** is **** **** *********** than ********, ***** *** analytic ****** **** ***** or ********* ** *** person *** *** ******* time *****.

People ********

************* *** ****** ** people *** ***** *** leave ** **** ******* a **** *** ** important ** ******** ********** for ****** ********, *** valuable *** ********* ** retail *** *********** *********.

****** ******** ********* **** increment ** ********* ** people **** ******* * defined **** ** ****:

**** ** ******* ** tripwire ********* *** **** not ******* ** ***** for ***** ****** ********.

Object **** ****** / *****

****** **** ****** *** taken ********* **** ********* because ********** ******* (****, boxes, *********) **** *** left ****** *** ** dangerous ** ***** ********* and ********* ************* **** closures ** ****-******** ********* (airports, *******, ********):

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*******, ******* ******-****-****** *** taken ********* *** **** challenging ** ******* **********, you **** ** ** careful ** ****** **** scene, *** ****** ***** will **** **** *** analytic.

Beware: **** ** ********* **********

********* ***** **** ******** object ***** **** ****** for *** ************** ** human ********' ******, ***, clothing *****, ***. *******, in *** *******, **** the **** ********** ********* often *********** ******** **** demographic ***********, **** *** classified ** *****, ******** classified ** ******, ***.

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Where ********* *** *********

********* *** ** ********* by *******, *********, *******, or *** *****.

*** ********* ** ***** 4 ********* ***:

  • ******:******-***** ********* *** ** more ******** ******* **** can ** ********* ** low ***********, ****-******* *****, before ********. *******, ******** processors ******** *** ******** analytics **** ********* **** limited ** ********* ******* and/or *********** ************ (*.*. LPR/ANPR).
  • ********:********* ***** ** * recorder ********* ****** *** a **** ******** ********* than * ****** ****** could *****, ***** ** used *** *** ******* connected ** *** ********. Also, *** ********* *** immediately ********** **** *** viewing ********. *******, ******** manufacturers ** *** **** to ****-******* *** ******** to **** *** ********'* price ***, *** ***** the ********* *** *******.
  • ******:********* ********* ********** *** built ** **** ********* analytics ********** **** *********, and ********* ******* ******-**** components. *******, ******-***** ********* add *********** **** *** can ** ***** *** power-intensive.
  • *****: ********* ***** ** the ***** ****** ********* the ******** *********** ** recorders ** *******, *** provide **** ******** *********. However, ***** **********, ************ public ***** ******* (******, Google), *** ** **** expensive. *****-***** ********* ******** typically **** ******* $**-$** per ****** *** *****. Moreover, ******** ****** *** will ******** *** *** limit *** ****** ** cameras **** *** ** analyzed.

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

*** ****** ************* ** that **** ******** ********* to ***** ***** ************.

**** ********* *** * pre-trained ***** **** **** not ****** ***** ******** over **** ***** ** the *****. *******, * small ****** ** ******* and *******, **** ** Avigilon's ****-******** *** ******, attempt ** ***** *** scene **** *** ****** in **** * ****** of ****.

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

*** ******* ********** ** doing ** ****** ******* has **** *** **** of *** ******** ** run ** ****** *** camera. **** **** ** AI ******* **** ************ been **** *********. ******* of ****, ******* **** VMD *******, ******* *** deficiencies *** ********* *** tradeoff ** **** ****** cost *** *** ******.

*******, ******** ****** **** begun ********** *** *********** increasing *************, ******* ** a **** ** ***-**** models ******** '**', **** varying ******* (*** ********* ********* ******** ****).

GPU ******** ********* ** ******* *** *********

******* ** ********* ********* in ******* *** ********* are **** ******* **** camera-based ********* *** **** to ******* ******** ******* simultaneously, **** ******* * or **** *** ***** that *** **** ********* of ******* *** ****.

***** **** ***** **** that ** ********* *** generally *** ** *** reach ** **** ***********, making **** **** ****** a *** ** ****-*** commercial, *********, ** ********** projects.

Deep ******** / ** ********** ** ****

***** ***** ************ ********* has **** ********, ***** and ******** *** ****** 20 *****,**** **** ** *** year **** ** ******* goes **********,******* **** ****** *** manufacturers **** ** ******** fairly ******** *** ******** people ********* *********. *** the **** **** ** not *** ***** ** stand *** ** ********, the ***** ***. **** is *** ****.

Single ****** **. **** ****** ***********

******* ********* *** ** difficult ** ********* ****** 3rd *******, **** ******* focus ** ******* ***** one '*** ** ***' video ********* / *** solutions. *** **** ****** example ** **** ** Avigilon, ***** ****** *** video ********* *** ******* integrated **** ************ ****** ***. ***** ******* ******** include *** ******** ****** manufacturers (*.*.,*******'* ******) ***** ********* **** work **** ***** *** VMS.

***** *************, **** ** Dahua, *********, *** *******, also ****** ********* ***** analytics ** ***** *** recorders (********* ******, ******** boxes, *** *************) ***** third-party ******* ** ******* to *** ********* *** events ****.

Video ******** ******** ********

***** ** * **** of ******* ***** ******** providers.

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

  • ********: ********* **** **** a **** ***** ** the ******* *** * number ** *****.
  • ***** *** *********: ***** emphasis ** ****** ** to ************* ******* ***** low-cost *********.
  • **** *** ******: **** have **** ********** **** to ******* *********, *** are **** ******** ** bring ********* ** ******.
  • *****: **** ******* ***** performing ********* *** * number ** *****, *** typically ** ***** **** expensive ******. ******** **** offered **** ******** ********* in *****-**** *******.

********-*****:

  • ******** - **** ****** specialist ******** *** ** expensive, ******* ** ****** systems (*** ****** *******).

********:

***** ********* ** *** ** analytics ************ **** *** *****, mostly ******** ********* ** add-ons *** *****-****** *******. These *** ********* **** higher ****** ********* **** camera *************, *** **** often ***** *********** ********** (e.g. ****, *******, ********, etc.).

Comments (5)

***** ****, ****** ******** some *********** ********.

****** *********** *** ******* usually ***** * *** experience ******* *** ****.

************ ** ********* **** happened ***** ******** ** the ******* ******* ** meet **************** ****** *** *** increase ** ********* ********, a ******* ** *****’* Law.

*** ******* ** **** promise *** ***** ******* is ****** ******* **** analytics. ****** ** ****, not *********.

* ****** * ******* that ************ ******* *** environment (***** **** *** setup), *** ** *** a ******* ****** **** learning. ***? ** *** often **** ** ****** fence **** ************ *** after * ****** ** time ** ***** ****** what ** *** ******* to ******.

****** **** “******* ***.”

******, **’* * *** better **** ** *** and *** ** **** need ** ****** *** “human ******.”

**** **** *******. ***** how *** ***** *** hype ** ** *** reality ** **** ** shipping. * ******** ******* deep ******** ******* ********* learning ** **** **** on ** * ******** to *** ****. **** line ** ******* ** many *** *** **** type ******* **** ** Uniview *** ** ****** good ** ** **********. I ***** ***** ******* this * ***** **** a **** **** **** that * ***** ****** possible ***** ********* ********** to *** *** ******** the ****** ***** **. Thanks *** *******.

* ***’* **** ** see *** *** *** how ******** ********* **** come ** *** **** few *****. **** * great **** **** **’* a *** **** ********.

* *** *****:
*. *** ********** ******* operation ****** *** ** night. ** *** **** as * *****, *** people **** ** *** more ********** ** *****/****/***. Night ***** *** **** no ***** *** **** visibility - ** ***** you ****** ** ********** lighting ***** ** ******* uneven, *** ********* ****** and ******* ***********. **** your ****** ** ***** time/rain.
*. "****/***** *******" ********* is ****** **** *** would *****. **** **** to ************* ******* **** and **** '**** ****** was *****/******** ******".
*. **** **** ***** about ***** ******* - your ****** ********* **** the **** *******/*** *****. Once **********, **** ****** become ***** ********* *********. Raw ** ****.

** ****** ******* *******, occupancy ********* ** ********* tied ** ****** ** IN *** ** *** badge ****** ** ****** of *****. *** ********* are ******** ** ******** badge *** ** ***** the ********. * **** the **** ** ****** the ****** **** *** enters ** ******, *** analytics *** ********* **** based ** ********* ** movement, *** **** * running ***** ** *** many ****** *** ** the ********. **** :)

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