Video Analytics Demographics Guide (Age, Clothing, Emotion, Gender, Race)

By IPVM Team, Published Apr 05, 2021, 10:10am EDT

This 21-page guide examines the fundamentals of common person demographic analytics.

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Inside we cover:

  • Machine and Deep Learning Demographics
  • Clothing - Easiest / Least Controversial
  • Clothing Object Detection vs Simple Methods
  • Video Surveillance Clothing Typically Simple
  • Clothing Color Common
  • Smaller Clothing Details More Challenging
  • Age Recognition
  • Age Recognition Issues
  • Age Recognition Declining
  • Gender Analytics
  • Face and Person Detection Gender Classification
  • Gender Analytics Issues
  • Emotion Recognition
  • Risk of Using Appearance to Determine Emotion
  • Race/Ethnicity
  • Race and Ethnicity Controversial
  • "Political Leaning" Detection Research

This is the study guide for Class 11 of IPVM's Video Analytics Course.

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

***** **** ***** ************ analytics ****** *********** ******* (e.g.*******, *****, ********), ******* *** **** learning ********** *** ************ trained ** ****** *** classify *********** ******* ** those *******.

*** **** ****** ************ classified ** ***** ************ include:

  • ********
  • ******
  • ***
  • *******
  • ****/*********

*******, ******* *********** ********* in **** ** ************* lower **** ******, ****, and ******* *********.

******** *** ****** **** for ********* ********* ***** of ************ *** *** less ****** **** ****** detection ******** (*.*.****), *** **** *** not ******** *** ********** use.

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

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Clothing - ******* / ***** *************

******** ***** *** ***** are **** ** ******** search **** *** **** locate * ****** ***** on ***** **********. ******** analytics *** *** ***** controversial ******/*********** ******, ** not ******* * **** level ** ******* ******, and *** **** *** easiest ******** *****.

Clothing ****** ********* ** ****** *******

******** ********* *** * common *******; ****** ********* (e.g. "** * *** detected?") ** *****-***** ******* of *** ******* *** worn (*.*. "**** *** above *******"). **** ** typically ******** **** * multi-step *******, ******** **** person *********. **** ********** trained *** ******** ********* process **** ****** *****.

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**** ******** ********** *** trained ********** ** ********* ** images ** *********** *** ********* **** accurate **** ***** ***-***** person ******** (*.*. ***, emotion). **** ******** ******** detection ********** *** ************ to **** *** ****** or *********, *****, *** corners, ** ********** ******** pieces:

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******* ******** ******* *** hard-coded, ***** *** ***** of *** *** **** of * ****** *** the ***** *** *** bottom **** *** *****:

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******* ******** ******* ********* have * ***** ************* cost *** *** ***** similarly ******** ** **** learning ******** *********, ****** while ******** **** ******* details. ************, ******* ******** methods ******** **** **** deep ******** **** *********** angles *** ********* ******** persons, ****** ***** ************ video ******.

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

************ ******** ********* ********* give ****** *******, ********** such ** *** *** bottom ******. ********, *****-***** clothing ******* *** ********* limited ** * *** accessories **** ****, *******, and *********:

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***** ******** ********* ** academia *** *** ****** use-cases *** ** **** complex:

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******* ******** ********* *** not ******* (*.*. ******** vs *******) ** ***** surveillance, ** **** *** not ******** **** ****** safety ******** *** ******* searching *** ******** ** interest. ********, ******** ********** decreases ************* ****, *** the ***** ***** ** detail ******** ** ********** clothes **** **** ******* categories **** ******** ********* accuracy.

Clothing ***** ******

***** ** ** ********* detail **** ********* *** a ******** ******. **** algorithms ******** ***** *** lower **** ***** **********:

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***** **** ***** ***** filtering, ************, **** ********** classify *** **** ** clothing (*.*. **** ** short ****** ******):

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***** ************** ******* **** adjusting *** ********, ********** low *****, ** ***********, and ********* ********. *** IPVM's***** ********* ******** ******* / ******** *********** ******** ****.

Smaller ******** ******* **** ***********

***** ***** *** ***** body ******** ***** *** type *** ********* ******** in ****-*** ************, **** complex ** ******* ******* are ************* **** ***********. Glasses, ******, **** *****, or *** ********* *** typically **** ***** ******** than ***** ****** *********** details:

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Age ***********

*** *********** ********* *** machine *** **** ******** algorithms ** ******** ******** by *** ** *** category (*.*. *****, *****, elderly). *** *********** *** be **** ** ****** searches ** ** ******* general *********** ***********.

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** ** ***** **** to ****** ********** **** child ** ***** ** opposed ** ***** ****, and, **** **** ***** categories, **** ******* *** shown ** ** ** inaccurate.

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

******* ******** ********** ********* split **** ***** ** adult ** ******* ** ages, ***** ****** *** facial ******** *** ******. With **** ******** *** recognition, ***** ** ** way ** ******* ******* what ******** ** ********* uses, *** ** ** not *********** ** ****** as **** ***** ** other ************* ******** * human ***** *** ** determine * ******'* ***.

**** ******* ****** **** category-based ************** ******** * strong *********** ******* * person's ****** (** ********* on ******) *** ***, without ********* ** ****** features. ***** ***** ** systems ****** ** **** accuracy **** ******* ****** as **** ** ******** detected *** **** *** camera ** *** ***** densities:

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**** ******** ********** **** classify *** ** * specific ****** ** ***** of **** ****** *** a ********* ** ****** features:

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***** ********** ********* ******* higher ****** **** ********-***** age *********** *** **** with * **** ****** computational ****. *******, ******* of *** ********** ****** and ******* **********, **** are **** ****** ** determine ** ***** ** a ***** *** **** versa.

Age ************** ********* - ******* *** ******** ******

*** ************** ** * declining ***** ** ***** surveillance (*.*.**** *************), ****** *** ** a *********** ** *****, privacy ********, *** ******** problems. ************, ******** ********** is *********** *** ******** diverse *********** ** ******** studies **** ***** ********** ******** *** *********** for ********* *****.

***********, ******* ** ****** markets, ***** *** ******* surveillance ************ ***** *** provides ******** ********* ** alerting *********.

Gender *********

****** ********* *** ****** unsophisticated, ** ***** ** IPVM *******, ********* ****** primarily ***** ** **** length (******* **** ********** as ***, ****** **** as *****) *** ******** (skirts, ******* ** *****). Body ***** *** **** generally *** *** ***** impact ** ********** ** IPVM *******.

Face *** ****** ********* ****** **************

****** ************** ** ** extension ** ****** *** face ********* (******* ******/****/******* *****). *** ****** ********* that ******* **** *** person-based **********, ************** ** much **** ******** **** a **** ** ********:

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**** ** ****** *** to *** **** ****** level ** ***** ****** when * **** ** detected, *** **** ********* gender ********* ********* **** on **** ******.

Gender ********* **********

****** ******** ** ************* challenging, ********* ******* ***** physical *********** **** ****** (e.g. ******, ******, ****, clothing). ****** ** **** difficult *** *********** ************* for ****** ******** *****.

*** *******, **** ****** is ******** **** ** categorize ******, ***** **** a **** **** ** males **** **** **** detected ** ******:

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**********, ******** **** ***** hair ** ***** ********** will ** ******** ** male:

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******* ** ******** *** cultural ******** ** **** length, ***** ***** ** methods *** ******** ** be ******** ** ******* populations.

************, **** ******* ********* that **** ****** ********* algorithms ***** ******** ** classify **** ** ******:

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*******, **** ********* *** thresholds ** ****** **** high ********** ****** ******* and ** *** ****** gender *** **** ******** people, ************ *** *******.

Emotion ***********

******* *********** ********* ******* a ******’* ******* ***** on ***** ****** ********. It ** **** ****** in ***-******** ***-***** **** as *** ********* *** survey **** ********** ********.

******* *********** ** **** with **** ******** *** academics ***************** ******* **** * classes: *****, *****, ***, disgust, ********, *** ****.

************** *********** ***** **** "*****" is *** ******* ******* to ********:

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Risk ** ********** ** ********* *******

********** ** **** ** indicator ** ******* *** emotion ** ** ******** state, ** ***** ********* have * ******* ******* and ****** ***** ** hard ** ******, ****** spoofing ****.

** ****, ******** ****** expressions **** ***** *** happiness ****** ** ******, despite *** *******’* ********* state ********* *********:

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****/*********

**** *** ********* ********* use **** ******** ********** to ******** ****** ***** on **** ***** *** facial ********.

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***** *** ************* *** are **** ******* ** China. **** ********* ********* are **** ** ***** and ****** *** ****** minority, ***-***** ***********/**********, ** all *********** ****** ****** ** China.

***** ***** ********* **** been **** ** *** West,********* **** **** ********* from *** **** ** the ****, ******* ********* *** countries *** ********** ********** from ***** ********* *** to ***********.

Race *** ********* *************

**** *** ********* ********* are ************* ******* **** are **** *** **************. These ********* **** ************** easy *** *** **** automate **, ** ** being **** ** ********.

** ********, **** *** no *** ********** (***** is *** **** *** skin ********* ** ******** as * *** ** measuring ******* *********) ** creating ****-***** ********** ** in ****** *************. ***** categories ****** **** ****** to ****** *** ****** to ******, ** ** is ********** ** ****** an ******** ********** ** race *** *** ********** of **** **** ** wrong ** **** ******.

"Political *******" *********

********** ********* **** ****** of ********** (******* **% of *** ****) ********** subjects ********* ******** *** video *********, *** *** study ******** **** **% American, ***** ****, ***, and ****** ************* **** ********* ****** according ** *** ********.

*** ***** **** **** learning ** ****** ***** (****** ****++) *** ******* ****** features (********) *** **** ***** them **** ************ **. liberal ***** ** * machine ******** ********** ********** model.

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******* **** ******** *** used, *** ******** ******** used ** ****** *** subjects *** *** *****, race, ***, *** ****** markers ***** **** **** used *** ** *** demographic *********** ** *** United ******.

Comments (3)

********** ***** ********* ** determine **** ******** ********* orientation **? ******* ***** go ***** **** ****! Its *** **** **'** already ****** ********* ********** in *** *********, *** a ******* ******* ** our ****** *** ********.

Agree
Disagree
Informative
Unhelpful
Funny

**** **** **** "******". I ***** ** *** want ** **** **, you ****** **** ** have * ****** ** statistics :)

Agree
Disagree
Informative
Unhelpful
Funny

********* *******, "**** * joke".

**** ****** ** ******* and ********, **** *** be **** ** *** Party *** ******* *** country.

Agree
Disagree
Informative
Unhelpful
Funny: 1
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