Video Analytics Advanced Objects / Behavior Recognition Guide

By IPVM Team, Published Mar 23, 2021, 01:22pm EDT (Info+)

This guide examines complex offerings of advanced object classification and behavior recognition algorithms.

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

  • ******* *** **** ******** ******** ****** Detection
  • *** ********* / **********
  • ********** ******* ******** **** *********
  • ******* ******** **** ********* ****** *******
  • **** ******** **** ********* ********
  • *** *********
  • ****** **** ****** ** ***** **********
  • ***** *********
  • ****** ********* ****** **** ** / Reflected ********
  • ******** ***********
  • ******** *********** *********** ****
  • ******** *********** ******* - ****** *********, Pose **********
  • ***** ******* ** ******** ******* *****
  • ********** *** ************ ******** ********
  • ******* ******** *********
  • ******** ******** *******
  • ***** **** *** *****

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

******* *** **** ******** ********** *** trained *** ********* **** ********* ***** of ******* ** ***** ************. ************, ********, *** ******** *** **** ******, **** **** detect *** ******** ******** *******, **** commonly ** ***** ************:

  • ****
  • ****
  • *****
  • ***** ** *******

***** ***** *** *** **** ****** in ***** ************, ******** (*.*.****) *** ****** **** *** ********* many ********* ***** ** *******:

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*** ****'****** ********* ************ ******** ********** *********** ***** ******* ******** and **** ******** ******* *** ****** training ********.

Gun *********

*** ********* ********* *** ** ***** authorities ** ******* ******* *** **** locate ************ ****** ****** ******* *********. Deep ******** ********** *** ******* ** thousands ** ****** ** ********* ***** of ****, ***** *** ******** ******* by *** ******** ********* ******* ***** are ** ******** ********* *** ********.

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******* ****** ********* ****** *** * common ****** *** ******** ******* *** angles *** ******* *** ******* **** video ************ *******:

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Gun ********* **********

*** ******* ********** *** *** ********* are **** **** ****** ******* ****** as **** (*.*. ******, ******, ****** mugs), **** **** *** *****, ****** concealed, *** *** **** ****** **** are ********* ** *** ******* **** clothing.

*** *******, ** *********** ** * school **** ******** ** ******** * drill ** ****** *** ******* ********:

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********, *** ********* ***** ****** ***** significantly ****** ***** ******* ** *** inherent ******/********* *** ********* ********** ********* or ******** ** ****-******** ********* (*.*. airports, *******, ********).

Simplistic ******* ******** **** *********

**** ********* ** ** ********* ** face ********* (******* ******/****/******* *****). ** ********** ******* ********-***** *******, first, *** **** ** ********, *** secondly, ** ****/*****/**** **** ******** *** not ********, *** ********* ********** **** a **** ** ***** ****:

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* **** ****** *** ********* ********** method ** ***** **** ********* **** fails **** ***** *** ****, *** alarming ** * ****** *** * face ** ********:

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** ****, ******* **** ****** ***-**** mask ********** ******** **** ***, ** is ****** *******.

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

******* ********** **** ********* ***** *** faces ******* ***** **** ********, ********** methods ** ******** *** ***** **** of *** **** **** ** ******** as ****-*******.

******** **** **** ******* **** ***** mask ********* *** ******, ***** *****, and *****:

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********, ******** ** *********** ****** **** covering **** ** ******** ** *****:

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***** **** **** ********* *** ******** rapidly **** *** ****** ** ***********-****** mask-wearing ******** ** **** ***** ** the *****, ******* **** ********* ********* are ******** **** **** ** ******** entry, ****** **** **** ***** **** off ** *** **** ** *** building **** *** ** ********, ******** its *****.

*******, *** ************* **** ** **** lower **** ********* ******** ********** **** learning **** **********.

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

**** ******** **** ********* ** ******* on ****** ** ****** ******* *****, to ***** **** ***** **** ***** look ****, *** **** *** **** of ***** ****** ********.

***** **** ***** ******** *** ***** developed*** **** ******** ********* ******** ** response ** *********** **** ********:

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** ****, **** ***** **** ******** mask ********* **** ****** ** ***** by ******** *** ****:

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*******, ***** **** ******** **** ********* is ******* ** ****** *****, **** testing *** ***** **** ********** *** not ******* ** *********** ** * mask ** ***** ******** ****, ******** the **** *** *****:

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************, **** ******** **** ********* ** much **** ****** **** ******* ******** detection *** ** ********* ******** ************, generally ***** ** ****-**** ******* *** appliances.

Bag *********

*** ********* ********** *** ********* **** learning ********** ******* ***** ****** ******** like****, ***** ******** ********** *** ******** and *********:

** **** ***** **** ******** *********, datasets *** ******** *** ****** ********** algorithms, *** **** ******* (*,*,*) *** ***** ********** ******** ** bag *********:

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******** ********* ***** ******* ********* ** bags ** ** ********* ** ****** detection, *** ** ** *********** **************:

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**** ** **** ****** ** ******** as *** ********* ** ******** ************** only ** * ****** ** **** detected, ***** ************* ********* *** ********** that *** ****** ** * *** or ********. *******, *** ********* ** an ********* ** ****** ********* **** significantly ****** *** *** *** ******-****-****** or *****, ***** ** *** ********* marketed *** ******** ******** ************.

Object **** ****** ** ***** **********

****** **** ******/***** ** *** ** the **** ****** *** ********* ***** in ***** ************. ***** *** ******** for ****-********* ** ****** ******, ********* in ******* ***** **** ******* ********, reflections, *** ****** ******** ******.

******* *** **** ******** **** *********** challenges *** ******* **** ****** ** taken. *********, ** **** ********** ******* left ****** ** ***** *** ******. However, ***** ** ** **** *** humans ******** ******* ** ****** *** **** ******, it ** **** ********* ** ********* if *** ****** ** * ******, and *** **** ** ***** ****** is ***********.

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***** ***** *** ** *********** *********** to **** ******* * **** ******** bag ********* ** ******* ** ***, the ****** ** ********* *******, ******, and ******** ** ****** **** ** offered ** ****** ********.

** ****, ********** **** ** ****** proprietary ********, ***** *** ********* *** difficult ******* ** *** ***********.

*********** ****-***** ******* ****** **** ***** to *** *** *** ******** ********. Because ** ***** **********, ******-****-****** *** taken ********* *** ********* *** ******.

Grayscale ****** ********* - ***** ************** ******* ****** *********

**** ** *** **** ***** ********* use ********* ****** *** ********* ******* because ** ******* *** **** ** the ***** ***** ********. **** ***** analytics *** ***** **** ** * second **** ******* ******** **** *** camera's ******, ** **** ******* *************** of ***** *****.

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***** *********** ********** **** *** ***** values ** *** ******** ******* *** output * ****** *****. *******, *** range ** ****** ********** ** ********* 16 ** *****; ******* ******, *****, grays, *****, *****. **** ****** ****** with **** ******* ****** ***** ********** as ***** ** *****.

***** ** ** ********* ****** *** identifying ** ****** **** ********* *** a ******** ****** ** *******. ***** analytics *** ** ******* ** ********** or ** *********** **** ***** ******* (clothing, ******* *****):

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*******, ***** ************** ******* **** ********* for ********, ********** *** *****, ** illuminated, *** ********* ********.

Issues ********* ****** **** **

**** ************ ******* ******** ***** *** white ****** ** *** *****, ** mechanically ******** ** ** *** ******. This ***** **** ***** ****** *** typically *** ******** ** *** ******** / ** *****, ***** ****** ***** analytic *********.

**** ****-******* *** ***** ******** ******* white:

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* ***** ****** ** ************* ************ **** *******/***** ***** ********, ***** ********** **** **********, ** short ********* (**' / **):

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****** ****** ****** ******** ** **** scenes **** ***** *** ***** ***** LEDs, ****** **** **** *********** *** lost, **** ** ***** ***** *** grays *** *********** ******* ****** ****** and *****. *******, ****** *** **** clearer **** ******* ***** *** ****, where **** *** ********* *****************.

Reflected ********

******* ******** ********* **** ****** ******** in *** ********, *** *** ***** and ***** **, ** ********* ******* lighting **** ******* ***** ******, ***** objects **** *** *** *** ** appear ***:

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**** ***** ******** * *********** ****** of *****-******** ******* *** *** ******* searches, ****** ****** ***** *** ********* vehicles, ******** ********* *** *********** *** users.

**** ** ********* *** ** ***** in *** ******* ** ****-*** ***** because ***** ****** *** *** ********* bright ****** ** ***** ****** ** trick ** ********* **** *** ****** vehicle ** ***.

Behavior *********** ********

******** *********** ********* *** ********* **** to ******* **** **********, ******* ************* ** ******** ********** (*.*. *******, fighting):

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*******, ******** *********** ** ********* ******* very ********* ********* *** **** ******* attributes (*.*.******** ** ******* ** *******):

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********* **** ****-**** ******** *** ********* easy ** ****** *****, ** **** humans *** ******* *** ****** ********* the *********** ******* *******, *******, ** fighting/aggressive *********.

*******, **** *******, ******* ******* (*.*. tampering **** *** **** *******, **** counting ** * ******, **** ******** errors ** * *****-**-****) *** **** more ********* ** ********.

Behavior *********** *********** ****

**** ** ********** ********** ********* *** vary. ************ ************* *** ****** *********** analytics **** ** **** ********, ****** entrance *** ******* ** **** ** behavior ***********:

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***** ******* *** *** ********* *********, just *** ******** ** * ****** in ** ****.

Behavior *********** *******

******** *********** ********* **** * ******* methods; ****** ********* *** **** **********.

****** *********

****** *********-***** ******** *********** **** ******* and **** ******** ****** ********* (******* ****** / **** / ******* Guide), ******** **** ********** ***** *** movement, **********, *** ********** **** ***** objects (*.*. ****) ** ******** ********:

**** **********

**** ********** **** **** ******** ** create * ********/*****-****** ************ * *****, which ** **** ** ***** ******** and *********** **** ***** ******* ** classify ********:

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**** ********** ** **** *******, *** commonly ******* *** ********* **** *** not ******** ** ************, **** ******, the ******** *** *** ***** ** the ******** *** ******** *** **** relevant. ******* **** ** *** ******* is **** ******* ******* ***** **** typical ************ ******* ****** ******** **** using **** ************ *******.

******** **** ********** ** **** *************** difficult *** ********* ******** **-*****, ***** is *-** ****** ***** ******* **** object-based *********.

Human ******* *****

*** **** ****** ****** *** ******** behavior *********** ******* ** *****-******* ***** based ** **** **********, ****** *********, and ***** **** ** ******* ******.

********** ****** **** ********** ** ******** are, ** ** *** ******* ***** (e.g. * ****** ****** ******* *** colliding **** ******* ******) ** * gun ******** ** * **** (** opposed ** * ****** ** *******), and **** **** ******** ******* ** alert ** *********:

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**** ******** **** **** *** ******** than ********-******* *****, *** ***** *** clear/make ***** ** *** *******, *** only *** ******** ****** **** ** defined **** ******* ** *****.

Computer ******* *****

********* **** ******* ****** *** ****** of **********, **** ** ********, ***** with ******** ** ******* ****** ******* the ********, **** ** ****** ******* or *******, *** ********* *** *** optimal *** ** ******** ****:

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********* *** ******** ** **** *********** ways **** * ******, **** ** differentiating ******* ********* **** ******** ********* based ** *********** ********* ******* ****-*****, speed ** *** ******, *** ***** before *** *****.

********-******* ***** ******* * *** ** training, ***** ********** ****** ** ****** they *** *** ******* ***, *** learn ****** **** ****** ***. * computer-learned **** ***** ******** ** ********* of ******** *** ******** ********, *** faked ** **************** **** **** **** to ********** **********.

Supervised *** ************ ********

******** *********** ** ******* **** ********** methods ********* ******** ********* *** ************ methods *** ********* ******* ********.

Supervised *******

**** ***** ************ ******* *** **** learning ******** ******** ** **********, ******* the ******** ****** *** ***** *** labeled, *** *** ******** ******* **** details/values **** ** **** ** ****** the *******.

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** ******* ** ******** ********* ************ *****, ******** ******* ******** *** ******** for ******* ******** ** ****** *** correct **********/*********.

Unsupervised ******** **********

************ ******** ****** ** *** **** being *********, *** ********* **** ********** elimination. **** ************ ********** *********/***** *** background ** * ***** ******* ** does *** ****** ** ******* *** information ***** ** ********:

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************ ******** ** **** *** ******* and **** ******** ********* ******** *** often **** ********** **** ******** ****** detection ****** ** ******** ********.

************ ******** *** *** *** ** pixel ******-***** ********** **** **** *** unusual ******** ******* *******. **** *** the ******** ** **** ** *****-******** from ******** *** ******* *******.

********, *** ******* ** *** ************* between ********* ****** *********, **** ** a **** ***** **. * *****, but *** **** **** *************** ********* to ***.

Unsupervised - ******* ******** *********

************ ******* **** *** *******/******** ******** as ******* ** ****** ********. ************ learning ***** *** ********* ** ******* with ********* **** *** ******* **** is *******/********** ** *** ***, ******* human *****. **** ********* ******** **** or ***** ** ********* ********, *** example, *** *** ****** ** ***** what ** * ****** ******** ******* a ****, ** ***:

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*******, **** ***** ****** ******* ********, such ** ******* **********, ** *** differentiated **** ********* ******* ********, **** as ******* ***** *******.

*** *******, ***** ******* ********* ******* of * **** *** ** * sign ** * *******, ** ** also * ****** ******** ** ******* waiting, ******* ** ***** *****:

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** **** ***** ******** *** ********* behavior, **** ** ******** ** ** area **** **** ** ******, **** be *******. ******* ******* ******** ** scene-dependent, *.*., ******* ** ****** ** streets **** ** ******* *** ******** in ******* **** ********** ********** *******, the ********* **** ** ***** **** is ****** *** **** *****, ***** is ****-*********. *** *******, ********'* ******* behavior ********* ****** **** *-* ***** to ***** ****** *******.

************ ******** ***** **** ** ****** non-context-dependent **************, **** ** ******* ******** large ******* **** ** ****** ********, than **** *********** *******-********* ********* **** as ******* ** * ******* **** of ******* ******** ******* **** * building.

Behavior ******** *******

******** *********** ** *** ** ********** or ** **** ********** ** ***** analytics *********** *** ******* *** ************** ** real-world ************ ***-*****. ** ****, **** behavior ******** ********** ****** ***** *** datasets, ***** ********** ******** ********* ****** from ******** *** ****** *****.

Train **** *** *****

******* ********* ***** ********* ** ***** your *** ***** ******* ******* ******** knowledge, ********* ******-***** ****** *** **-**** tools ** ***** ****** **** *** be **** ** ******** *******.

***** **** ******

******************** ****** ****** *********** ****** *** deep ******** *******. ** ***** ******** both ******* ** ** ******, *** they **** **** ******** ** *** conditions **** **** ******* ** **** good ******** *** ******** ****** **** the ***** ** **** ** ** object *******.

**************** ** ******* **** ** ********* environments **************** **** ***** ****** ** ******.

******* **********

****** ***********,***** ****** **,****** ******, *************** "**-**** ******** ****** *********", ******* users ****** ****** ****** *** ****** detection **** ***** *** ****. ***** models *** ********* ** *** ***** or *******, ********* *** **-*******.

***** **** *** ********* **** ******** than ******-***** *********, **** *** **** complex ** ***.

Comments (4)

***** *******, *** **** ******. * was **** ** *** ***** **** the **** **** ******* * ***** of ******** ******* ** ******** *******, as * **** * ******** *** wants ** **** * **** ** on-site *****, *** *** ****/****/******* ******** analytics. **** ** **** ** ** it, **** ** **** ** **** it ** ***.

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**'* ******* *** *** ************ **** has ****. *** **** ** *** for **** ********** *** **** *** marketing ********* ** ****

Agree: 1
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Funny

******* ***** ******* ******!

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Funny

* ***** ****** ******* ********* **** providers **** ***** ******** ***** ** person ********* *** ******** :)

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