Video Analytics Advanced Objects / Behavior Recognition Guide

By IPVM Team, Published Mar 23, 2021, 01:22pm EDT

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

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

  • ******* *** **** ******** Advanced ****** *********
  • *** ********* / **********
  • ********** ******* ******** **** Detection
  • ******* ******** **** ********* Easily *******
  • **** ******** **** ********* Stronger
  • *** *********
  • ****** **** ****** ** Taken **********
  • ***** *********
  • ****** ********* ****** **** IR / ********* ********
  • ******** ***********
  • ******** *********** *********** ****
  • ******** *********** ******* - Object *********, **** **********
  • ***** ******* ** ******** Learned *****
  • ********** *** ************ ******** Training
  • ******* ******** *********
  • ******** ******** *******
  • ***** **** *** *****

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

******* *** **** ******** algorithms *** ******* *** detecting **** ********* ***** of ******* ** ***** surveillance. ************, ********, *** ******** *** **** ******, they **** ****** *** classify ******** *******, **** commonly ** ***** ************:

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

***** ***** *** *** most ****** ** ***** surveillance, ******** (*.*.****) *** ****** **** for ********* **** ********* types ** *******:

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*** ****'****** ********* ************ ******** ********** *********** ***** machine ******** *** **** learning ******* *** ****** training ********.

Gun *********

*** ********* ********* *** to ***** *********** ** violent ******* *** **** locate ************ ****** ****** shooter *********. **** ******** algorithms *** ******* ** thousands ** ****** ** different ***** ** ****, which *** ******** ******* by *** ******** ********* because ***** *** ** publicly ********* *** ********.

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******* ****** ********* ****** are * ****** ****** for ******** ******* *** angles *** ******* *** usually **** ***** ************ cameras:

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

*** ******* ********** *** gun ********* *** **** many ****** ******* ****** as **** (*.*. ******, phones, ****** ****), **** guns *** *****, ****** concealed, *** *** **** colors **** *** ********* to *** ******* **** clothing.

*** *******, ** *********** in * ****** **** commonly ** ******** * drill ** ****** *** through ********:

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********, *** ********* ***** alerts ***** ************* ****** risks ******* ** *** inherent ******/********* *** ********* cautionary ********* ** ******** of ****-******** ********* (*.*. airports, *******, ********).

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

**** ********* ** ** extension ** **** ********* (see**** ******/****/******* *****). ** ********** ******* learning-based *******, *****, *** face ** ********, *** secondly, ** ****/*****/**** **** features *** *** ********, the ********* ********** **** a **** ** ***** worn:

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* **** ****** *** similarly ********** ****** ** using **** ********* **** fails **** ***** *** worn, *** ******** ** a ****** *** * face ** ********:

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** ****, ******* **** common ***-**** **** ********** function **** ***, ** is ****** *******.

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

******* ********** **** ********* looks *** ***** ******* lower **** ********, ********** methods ** ******** *** lower **** ** *** face **** ** ******** as ****-*******.

******** **** **** ******* that ***** **** ********* are ******, ***** *****, and *****:

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********, ******** ** *********** facial **** ******** **** be ******** ** *****:

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***** **** **** ********* use ******** ******* **** the ****** ** ***********-****** mask-wearing ******** ** **** parts ** *** *****, because **** ********* ********* are ******** **** **** in ******** *****, ****** that **** ***** **** off ** *** **** of *** ******** **** not ** ********, ******** its *****.

*******, *** ************* **** is **** ***** **** generally ******** ********** **** learning **** **********.

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

**** ******** **** ********* is ******* ** ****** of ****** ******* *****, to ***** **** ***** with ***** **** ****, not **** *** **** of ***** ****** ********.

***** **** ***** ******** are ***** ************ **** ******** ********* training ** ******** ** coronavirus **** ********:

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** ****, **** ***** deep ******** **** ********* much ****** ** ***** by ******** *** ****:

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*******, ***** **** ******** mask ********* ** ******* at ****** *****, **** testing *** ***** **** algorithms *** *** ******* of *********** ** * mask ** ***** ******** worn, ******** *** **** and *****:

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************, **** ******** **** detection ** **** **** common **** ******* ******** detection *** ** ********* hardware ************, ********* ***** in ****-**** ******* *** appliances.

Bag *********

*** ********* ********** *** typically **** ******** ********** trained ***** ****** ******** like****, ***** ******** ********** for ******** *** *********:

** **** ***** **** learning *********, ******** *** critical *** ****** ********** algorithms, *** **** ******* (*,*,*) *** ***** ********** accuracy ** *** *********:

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******** ********* ***** ******* backpacks ** **** ** an ********* ** ****** detection, *** ** ** independent **************:

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**** ** **** ****** to ******** ** *** algorithm ** ******** ************** only ** * ****** is **** ********, ***** significantly ********* *** ********** that *** ****** ** a *** ** ********. However, *** ********* ** an ********* ** ****** detection **** ************* ****** the *** *** ******-****-****** or *****, ***** ** bag ********* ******** *** critical ******** ************.

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

****** **** ******/***** ** one ** *** **** common *** ********* ***** in ***** ************. ***** are ******** *** ****-********* or ****** ******, ********* in ******* ***** **** dynamic ********, ***********, *** nearly ******** ******.

******* *** **** ******** face *********** ********** *** objects **** ****** ** taken. *********, ** **** situations ******* **** ****** or ***** *** ******. However, ***** ** ** easy *** ****** ******** ******* ** ****** *** left ******, ** ** very ********* ** ********* if *** ****** ** a ******, *** *** risk ** ***** ****** is ***********.

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***** ***** *** ** theoretical *********** ** **** objects * **** ******** bag ********* ** ******* to ***, *** ****** of ********* *******, ******, and ******** ** ****** what ** ******* ** common ********.

** ****, ********** **** to ****** *********** ********, which *** ********* *** difficult ******* ** *** application.

*********** ****-***** ******* ****** take ***** ** *** how *** ******** ********. Because ** ***** **********, object-left-behind *** ***** ********* are ********* *** ******.

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

**** ** *** **** video ********* *** ********* images *** ********* ******* because ** ******* *** size ** *** ***** being ********. **** ***** analytics *** ***** **** as * ****** **** analyze ******** **** *** camera's ******, ** **** numeric *************** ** ***** color.

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***** *********** ********** **** the ***** ****** ** the ******** ******* *** output * ****** *****. However, *** ***** ** colors ********** ** ********* 16 ** *****; ******* colors, *****, *****, *****, white. **** ****** ****** with **** ******* ****** being ********** ** ***** or *****.

***** ** ** ********* detail *** *********** ** object **** ********* *** a ******** ****** ** vehicle. ***** ********* *** be ******* ** ********** or ** *********** **** other ******* (********, ******* color):

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*******, ***** ************** ******* when ********* *** ********, especially *** *****, ** illuminated, *** ********* ********.

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

**** ************ ******* ******** black *** ***** ****** in *** *****, ** mechanically ******** ** ** cut ******. **** ***** that ***** ****** *** typically *** ******** ** low ******** / ** night, ***** ****** ***** analytic *********.

**** ****-******* *** ***** clothing ******* *****:

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* ***** ****** ** manufacturers ************ **** *******/***** ***** emitters, ***** ********** **** limitation, ** ***** ********* (20' / **):

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****** ****** ****** ******** in **** ****** **** using *** ***** ***** LEDs, ****** **** **** differences *** ****, **** as ***** ***** *** grays *** *********** ******* bright ****** *** *****. However, ****** *** **** clearer **** ******* ***** the ****, ***** **** are ********* *****************.

Reflected ********

******* ******** ********* **** causes ******** ** *** lighting, *** *** ***** and ***** **, ** reflected ******* ******** **** vehicle ***** ******, ***** objects **** *** *** red ** ****** ***:

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**** ***** ******** * significant ****** ** *****-******** results *** *** ******* searches, ****** ****** ***** for ********* ********, ******** confusion *** *********** *** users.

**** ** ********* *** an ***** ** *** daytime ** ****-*** ***** because ***** ****** *** not ********* ****** ****** or ***** ****** ** trick ** ********* **** the ****** ******* ** red.

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

******** *********** ********* *** generally **** ** ******* live **********, ******* ************* ** ******** ********** (e.g. *******, ********):

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*******, ******** *********** ** difficult ******* **** ********* behaviors *** **** ******* attributes (*.*.******** ** ******* ** Dancing):

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********* **** ****-**** ******** are ********* **** ** ground *****, ** **** humans *** ******* *** easily ********* *** *********** between *******, *******, ** fighting/aggressive *********.

*******, **** *******, ******* actions (*.*. ********* **** ATM **** *******, **** counting ** * ******, cash ******** ****** ** a *****-**-****) *** **** more ********* ** ********.

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

**** ** ********** ********** analytics *** ****. ************ manufacturers *** ****** *********** analytics **** ** **** crossing, ****** ******** *** exiting ** **** ** behavior ***********:

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***** ******* *** *** detecting *********, **** *** presence ** * ****** in ** ****.

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

******** *********** ********* **** 2 ******* *******; ****** detection *** **** **********.

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

****** *********-***** ******** *********** uses ******* *** **** learning ****** ********* (******* ****** / **** / ******* *****), ******** **** ********** rules *** ********, **********, and ********** **** ***** objects (*.*. ****) ** classify ********:

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

**** ********** **** **** learning ** ****** * skeleton/stick-figure ************ * *****, which ** **** ** track ******** *** *********** with ***** ******* ** classify ********:

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**** ********** ** **** studied, *** ******** ******* for ********* **** *** not ******** ** ************, like ******, *** ******** are *** ***** ** the ******** *** ******** are **** ********. ******* much ** *** ******* is **** ******* ******* lower **** ******* ************ footage ****** ******** **** using **** ************ *******.

******** **** ********** ** more *************** ********* *** generally ******** **-*****, ***** is *-** ****** ***** density **** ******-***** *********.

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

*** **** ****** ****** for ******** ******** *********** systems ** *****-******* ***** based ** **** **********, object *********, *** ***** data ** ******* ******.

********** ****** **** ********** of ******** ***, ** in *** ******* ***** (e.g. * ****** ****** quickly *** ********* **** another ******) ** * gun ******** ** * hand (** ******* ** a ****** ** *******), and **** **** ******** happens ** ***** ** generated:

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**** ******** **** **** for ******** **** ********-******* rules, *** ***** *** clear/make ***** ** *** creator, *** **** *** specific ****** **** ** defined **** ******* ** alert.

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

********* **** ******* ****** and ****** ** **********, such ** ********, ***** with ******** ** ******* scenes ******* *** ********, such ** ****** ******* or *******, *** ********* out *** ******* *** to ******** ****:

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********* *** ******** ** more *********** **** **** a ******, **** ** differentiating ******* ********* **** fighting ********* ***** ** complicated ********* ******* ****-*****, speed ** *** ******, and ***** ****** *** event.

********-******* ***** ******* * lot ** ********, ***** performing ****** ** ****** they *** *** ******* for, *** ***** ****** they ****** ***. * computer-learned **** ***** ******** or ********* ** ******** and ******** ********, *** faked ** **************** **** will **** ** ********** algorithms.

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

******** *********** ** ******* into ********** ******* ********* specific ********* *** ************ methods *** ********* ******* behavior.

Supervised *******

**** ***** ************ ******* and **** ******** ******** training ** **********, ******* the ******** ****** *** video *** *******, *** the ******** ******* **** details/values **** ** **** to ****** *** *******.

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** ******* ** ******** ********* ************ *****, ******** ******* ******** are ******** *** ******* learning ** ****** *** correct **********/*********.

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

************ ******** ****** ** the **** ***** *********, and ********* **** ********** elimination. **** ************ ********** eliminate/learn *** ********** ** a ***** ******* ** does *** ****** ** provide *** *********** ***** an ********:

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************ ******** ** **** for ******* *** **** learning ********* ******** *** often **** ********** **** learning ****** ********* ****** to ******** ********.

************ ******** *** *** VMD ** ***** ******-***** techniques **** **** *** unusual ******** ******* *******. This *** *** ******** of **** ** *****-******** from ******** *** ******* shadows.

********, *** ******* ** not ************* ******* ********* shaped *********, **** ** a **** ***** **. a *****, *** *** much **** *************** ********* to ***.

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

************ ******* **** *** unusual/abnormal ******** ** ******* to ****** ********. ************ learning ***** *** ********* is ******* **** ********* data *** ******* **** is *******/********** ** *** own, ******* ***** *****. This ********* ******** **** or ***** ** ********* learning, *** *******, *** the ****** ** ***** what ** * ****** normally ******* * ****, or ***:

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*******, **** ***** ****** unusual ********, **** ** garbage **********, ** *** differentiated **** ********* ******* behavior, **** ** ******* trash *******.

*** *******, ***** ******* loitering ******* ** * bank *** ** * sign ** * *******, it ** **** * normal ******** ** ******* waiting, ******* ** ***** phone:

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** **** ***** ******** but ********* ********, **** as ******** ** ** area **** **** ** fights, **** ** *******. Because ******* ******** ** scene-dependent, *.*., ******* ** normal ** ******* **** by ******* *** ******** in ******* **** ********** pedestrian *******, *** ********* need ** ***** **** is ****** *** **** scene, ***** ** ****-*********. For *******, ********'* ******* behavior ********* ****** **** 1-2 ***** ** ***** in**** *******.

************ ******** ***** **** on ****** ***-*******-********* **************, such ** ******* ******** large ******* **** ** office ********, **** **** complicated *******-********* ********* **** as ******* ** * certain **** ** ******* removing ******* **** * building.

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

******** *********** ** *** as ********** ** ** well ********** ** ***** analytics *********** *** ******* *** representative ** ****-***** ************ use-cases. ** ****, **** behavior ******** ********** ****** their *** ********, ***** collecting ******** ********* ****** from ******** *** ****** media.

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

******* ********* ***** ********* to ***** **** *** model ******* ******* ******** knowledge, ********* ******-***** ****** and **-**** ***** ** build ****** **** *** be **** ** ******** cameras.

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

******************** ****** ****** *********** models *** **** ******** cameras. ** ***** ******** both ******* ** ** simple, *** **** **** only ******** ** *** conditions **** **** ******* on **** **** ******** and ******** ****** **** the ***** ** **** of ** ****** *******.

**************** ** ******* **** or ********* ************ **************** **** ***** ****** on ******.

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

****** ***********,***** ****** **,****** ******, *************** "**-**** ******** ****** solutions", ******* ***** ****** custom ****** *** ****** detection **** ***** *** data. ***** ****** *** processed ** *** ***** or *******, ********* *** in-cameras.

***** **** *** ********* more ******** **** ******-***** solutions, **** *** **** complex ** ***.

Comments (4)

***** *******, *** **** timely. * *** **** in *** ***** **** the **** **** ******* a ***** ** ******** reports ** ******** *******, as * **** * customer *** ***** ** keep * **** ** on-site *****, *** *** trip/fall/unusual ******** *********. **** of **** ** ** it, **** ** **** to **** ** ** too.

Agree
Disagree
Informative
Unhelpful
Funny

**'* ******* *** *** surveillance **** *** ****. Not **** ** *** for **** ********** *** also *** ********* ********* as ****

Agree: 1
Disagree
Informative
Unhelpful
Funny

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

Agree
Disagree
Informative
Unhelpful
Funny

* ***** ****** ******* behaviour **** ********* **** reach ******** ***** ** person ********* *** ******** :)

Agree: 1
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Funny
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