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

Published Apr 05, 2021 14:10 PM

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

***** **** ***** ************ ********* ****** fundamental ******* (*.*.*******, *****, ********), ******* *** **** ******** ********** are ************ ******* ** ****** *** classify *********** ******* ** ***** *******.

*** **** ****** ************ ********** ** video ************ *******:

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

*******, ******* *********** ********* *** ************* less ****** **** ******, ****, *** vehicle *********.

******** *** ****** **** *** ********* different ***** ** ************ *** *** less ****** **** ****** ********* ******** (e.g.****), *** **** *** *** ******** for ********** ***.

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

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

******** ***** *** ***** *** **** to ******** ****** **** *** **** locate * ****** ***** ** ***** appearance. ******** ********* *** *** ***** controversial ******/*********** ******, ** *** ******* a **** ***** ** ******* ******, and *** **** *** ******* ******** *****.

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

******** ********* *** * ****** *******; object ********* (*.*. "** * *** detected?") ** *****-***** ******* ** *** clothes *** **** (*.*. "**** *** above *******"). **** ** ********* ******** with * *****-**** *******, ******** **** person *********. **** ********** ******* *** clothing ********* ******* **** ****** *****.

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**** ******** ********** *** ******* ********** ** ********* ** ****** ** clothing*** *** ********* **** ******** **** other ***-***** ****** ******** (*.*. ***, emotion). **** ******** ******** ********* ********** use ************ ** **** *** ****** or *********, *****, *** *******, ** categorize ******** ******:

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******* ******** ******* *** ****-*****, ***** the ***** ** *** *** **** of * ****** *** *** ***** and *** ****** **** *** *****:

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******* ******** ******* ********* **** * lower ************* **** *** *** ***** similarly ******** ** **** ******** ******** detection, ****** ***** ******** **** ******* details. ************, ******* ******** ******* ******** more **** **** ******** **** *********** angles *** ********* ******** *******, ****** video ************ ***** ******.

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

************ ******** ********* ********* **** ****** details, ********** **** ** *** *** bottom ******. ********, *****-***** ******** ******* are ********* ******* ** * *** accessories **** ****, *******, *** *********:

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***** ******** ********* ** ******** *** for ****** ***-***** *** ** **** complex:

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******* ******** ********* *** *** ******* (e.g. ******** ** *******) ** ***** surveillance, ** **** *** *** ******** when ****** ****** ******** *** ******* searching *** ******** ** ********. ********, limiting ********** ********* ************* ****, *** the ***** ***** ** ****** ******** by ********** ******* **** **** ******* categories **** ******** ********* ********.

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

***** ** ** ********* ****** **** searching *** * ******** ******. **** algorithms ******** ***** *** ***** **** color **********:

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***** **** ***** ***** *********, ************, some ********** ******** *** **** ** clothing (*.*. **** ** ***** ****** shirts):

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***** ************** ******* **** ********* *** lighting, ********** *** *****, ** ***********, and ********* ********. *** ****'****** ********* ******** ******* / ******** Recognition ******** ****.

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

***** ***** *** ***** **** ******** color *** **** *** ********* ******** in ****-*** ************, **** ******* ** smaller ******* *** ************* **** ***********. Glasses, ******, **** *****, ** *** detection *** ********* **** ***** ******** than ***** ****** *********** *******:

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

*** *********** ********* *** ******* *** deep ******** ********** ** ******** ******** by *** ** *** ******** (*.*. child, *****, *******). *** *********** *** be **** ** ****** ******** ** to ******* ******* *********** ***********.

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** ** ***** **** ** ****** categories **** ***** ** ***** ** opposed ** ***** ****, ***, **** with ***** **********, **** ******* *** shown ** ** ** **********.

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

******* ******** ********** ********* ***** **** child ** ***** ** ******* ** ages, ***** ****** *** ****** ******** are ******. **** **** ******** *** recognition, ***** ** ** *** ** knowing ******* **** ******** ** ********* uses, *** ** ** *** *********** as ****** ** **** ***** ** other ************* ******** * ***** ***** use ** ********* * ******'* ***.

**** ******* ****** **** ********-***** ************** exhibits * ****** *********** ******* * person's ****** (** ********* ** ******) and ***, ******* ********* ** ****** features. ***** ***** ** ******* ****** in **** ******** **** ******* ****** as **** ** ******** ******** *** from *** ****** ** *** ***** densities:

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**** ******** ********** **** ******** *** as * ******** ****** ** ***** of **** ****** *** * ********* of ****** ********:

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***** ********** ********* ******* ****** ****** than ********-***** *** *********** *** **** with * **** ****** ************* ****. However, ******* ** *** ********** ****** and ******* **********, **** *** **** likely ** ********* ** ***** ** a ***** *** **** *****.

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

*** ************** ** * ********* ***** in ***** ************ (*.*.**** *************), ****** *** ** * *********** of *****, ******* ********, *** ******** problems. ************, ******** ********** ** *********** for ******** ******* *********** ** ******** studies **** ***** ********** ******** *** *********** *** ********* races.

***********, ******* ** ****** *******, ***** are ******* ************ ************ ***** *** provides ******** ********* ** ******** *********.

Gender *********

****** ********* *** ****** ***************, ** shown ** **** *******, ********* ****** primarily ***** ** **** ****** (******* hair ********** ** ***, ****** **** as *****) *** ******** (******, ******* as *****). **** ***** *** **** generally *** *** ***** ****** ** algorithms ** **** *******.

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

****** ************** ** ** ********* ** person *** **** ********* (******* ******/****/******* *****). *** ****** ********* **** ******* face *** ******-***** **********, ************** ** much **** ******** **** * **** is ********:

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**** ** ****** *** ** *** much ****** ***** ** ***** ****** when * **** ** ********, *** face ********* ****** ********* ********* **** on **** ******.

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

****** ******** ** ************* ***********, ********* because ***** ******** *********** **** ****** (e.g. ******, ******, ****, ********). ****** is **** ********* *** *********** ************* for ****** ******** *****.

*** *******, **** ****** ** ******** used ** ********** ******, ***** **** a **** **** ** ***** **** long **** ******** ** ******:

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**********, ******** **** ***** **** ** short ********** **** ** ******** ** male:

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******* ** ******** *** ******** ******** in **** ******, ***** ***** ** methods *** ******** ** ** ******** on ******* ***********.

************, **** ******* ********* **** **** gender ********* ********** ***** ******** ** classify **** ** ******:

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*******, **** ********* *** ********** ** return **** **** ********** ****** ******* and ** *** ****** ****** *** most ******** ******, ************ *** *******.

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

******* *********** ********* ******* * ******’* emotion ***** ** ***** ****** ********. It ** **** ****** ** ***-******** use-cases **** ** *** ********* *** survey **** ********** ********.

******* *********** ** **** **** **** learning *** ********* ***************** ******* **** * *******: *****, *****, ***, *******, ********, and ****.

************** *********** ***** **** "*****" ** *** easiest ******* ** ********:

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

********** ** **** ** ********* ** emotion *** ******* ** ** ******** state, ** ***** ********* **** * partial ******* *** ****** ***** ** hard ** ******, ****** ******** ****.

** ****, ******** ****** *********** **** cause *** ********* ****** ** ******, despite *** *******’* ********* ***** ********* unchanged:

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

**** *** ********* ********* *** **** learning ********** ** ******** ****** ***** on **** ***** *** ****** ********.

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***** *** ************* *** *** **** outside ** *****. **** ********* ********* are **** ** ***** *** ****** the ****** ********, ***-***** ***********/**********, ** all *********** ****** ****** ** *****.

***** ***** ********* **** **** **** in *** ****,********* **** **** ********* **** *** used ** *** ****, ******* ********* *** ********* *** distancing ********** **** ***** ********* *** to ***********.

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

**** *** ********* ********* *** ************* because **** *** **** *** **************. These ********* **** ************** **** *** can **** ******** **, ** ** being **** ** ********.

** ********, **** *** ** *** definition (***** ** *** **** *** skin ********* ** ******** ** * way ** ********* ******* *********) ** creating ****-***** ********** ** ** ****** controversial. ***** ********** ****** **** ****** to ****** *** ****** ** ******, so ** ** ********** ** ****** an ******** ********** ** **** *** any ********** ** **** **** ** wrong ** **** ******.

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

********** ********* **** ****** ** ********** (correct **% ** *** ****) ********** subjects ********* ******** *** ***** *********, but *** ***** ******** **** **% American, ***** ****, ***, *** ****** are********** **** ********* ****** ********* ** PEW ********.

*** ***** **** **** ******** ** detect ***** (****** ****++) *** ******* ****** ******** (********) *** **** ***** **** **** conservative **. ******* ***** ** * machine ******** ********** ********** *****.

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******* **** ******** *** ****, *** physical ******** **** ** ****** *** subjects *** *** *****, ****, ***, and ****** ******* ***** **** **** used *** ** *** *********** *********** in *** ****** ******.

Comments (3)
UI
Undisclosed Integrator #1
Apr 06, 2021

********** ***** ********* ** ********* **** someones ********* *********** **? ******* ***** go ***** **** ****! *** *** like **'** ******* ****** ********* ********** in *** *********, *** * ******* erosion ** *** ****** *** ********.

Avatar
Hauke Kerl
Apr 07, 2021

**** **** **** "******". * ***** if *** **** ** **** **, you ****** **** ** **** * degree ** ********** :)

UM
Undisclosed Manufacturer #2
Apr 08, 2021

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

**** ****** ** ******* *** ********, must *** ** **** ** *** Party *** ******* *** *******.

(1)
JZ
John Zhu
Oct 25, 2022
IPVMU Certified

* ***** ***** ** * **** in *** ***** ********* **Race *** ********* ************* *******

********, ****** ** ********.

(1)