Gender Video Analytics Shootout

*******

***** ** *** *****, all ******* *** *** most ******* ****** **** length **** *********** ******, making ** ** *** the ******* ********* ** whether ******* **** ** classified ** **** ** female. *******, ***** ********* were ****** *************** *** routinely **** ******** **** an ******** ***** ***** not.

**********, **** **** *** the ***** ******, **** subjects ** ******* ***** and ******* ********** ** male ** ****** ***** on ***** *******, **** no ***** ***** ***** on **** ****.

***** ******* ****** ** manufacturer:

• ********'* ** ******* *** Appearance ****** **** ******* weighted ****, *********** ******** with **** **** ** female *** ***** **** as **** ** **** rates. ***** ******* **** as **** ****, ****** features, *** ******** ***** were *********** *******.
• ***** *** ****** ********* similarly, **** ******* ********* hair, *** **** ******** style, *********** ******** ** skirts ** ****** **** often. ************, **** * face *** ********, ****** features **** **** ******* considered.
• *********'* ****-**** **** *** camera **** ********* ****** analysis **** * **** was ********, ****** ****** which ********* ******** ** person *********, **** **** subjects **** ****** ****. Because ** *** **** PPF ******** *** *** camera ** ******* * face (~*** *** ** higher), ** *** **** likely ** ********* ******** gender.
• ******* *********** **** ********** hair ** *** ***** and ******* ******** *****, facial ********, **** ****, etc. ******* *** **** the **** ************ ***** sometimes *** *** ******** gender, ****** ********* ******** as * ****** ******* of **** ** ******.

Accuracy ********

******** ** ****** ** be ***** **** (**%+) in **** ************, ** men ********* **** ******* hair **** *****, ***, expect ******** *** ****** who ** *** *** this ********** ***, ** applications ***** *** ********** does *** ** **, accuracy ** ****** ** be ***** ****.

Performance ********

***** ** ** ******** of *** *********** ** all ** *** ****** recognition *********.

Avigilon *** ******* ********* **** ******

**** ****** *** **** heavily ******** *** *************** in *** ******* ** all ************* ** ********* (discussed *****).

******* *** ******** ******** hair *** ********. ** a ****** *** ******** with **** **** **** were **** ****** ********** as * *****, ********** of ***** *******, **** as ******* ******* ****** hair ** **** *****, shown *****.

*********, ******** ********** ****** effectively ******* ***** ******* and ********** ******** ******* weighted ** **** ******.

Dahua / ****** ****** **** ******* ** ****** *********

****** ****** ******, ***** and ****** ******** ****** on **** ****** ********* and **** *********, **** face ********* **** ********. When ***** ****** *********, hair ** **** ******** weighted.

*** *******, ***** ******* weighted ****** ** **** length **** ***** ****** detection, ***** ***** ********** as ****** ** **** was **** *** **** versa.

****** **** ******* ******** people ** **** ****** when ***** ****** *********, resulting ** ******* ***** classified ** ***** ** hair *** *****.

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

**** ***** **** ********, Dahua *** ****** ****** classification ****** **** **** accurate.

*** *******, *****, ****** considers *** **** **** long **** ** ** a **** **** * face ** ******** *** a ****** **** **** a ****** ** ********.

*********, ***** ********** **** person ** * **** when * **** ** detected *** * ****** when *** **** ** detected.

Body **** ******** **** **** **** ** *** *************

**** *** ****** **** types **** ******** **** and *********** **** **** short ** **** **** on * ******. ******, Dahua, *******, *** ******** would ***** ******** ******** as **** ** **** was ***** ** **** back, **** **** ***** female *******.

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

******* ****** *** ******** both ****** ****** ** long ****** / *** PPF ******, ****** ************** was ********** ** ****** distances ***** ******* ******** cannot ** *********. *** example, *** ***** ***** below *** ********** ********** as ****** ****** ** the ******, *** **** at * ********, ******* visible **** ****.

***** ********* ***** ** get * ****** *** when ********* ****** *** still ***** *** ** guess ****** ** ***** PPFs, ********* ** ***** classifications.

Skirts ******* ******** ** ****** *** *****

****** *******, ** * person *** ******** ******* a *****, ***** ***** automatically ******** **** ** a ******, ***** *****. Other ************* *********** *********** clothing *****.

************, ****** ******** ****** more **** ****, ** a ********* ***** * female **** ***** **** was ******** **** ****** it ***** ********* ** classified ** ****, **** a ***** *** ****, the ******* *** ****** classified ** ******.

Verkada **** ** **************

******* *** *** ******** gender ** *** ****** that **** ********. ** some *****, * ****** was ******** *** ***** to *******'* **** ** people ******** *** *** given * ******. *** other ********* ********** ****** whenever * ****** ** face *** ********.

Hikvision **** *** / ******* ***** ********

********* ****** *********** *** the **** ******** ** our ******* *** *** also *** **** ***********, only ******** ***** ** be ******** **** ** and ** **** *** (~90+).

Hikvision **** ***** ******

********* *** *** ****** hair ******* ******** ** other *********. **** ** long **** *** ******* with *** *******'* ****, they **** ********** ********** as **** ***** ****** would ******** **** ** female.

Versions ****

*** ********* ******** **** used ****** *******.

• ******** *.*-***-***-**: *.**.*.**(*****)
• ******** ******* ******: *.**.*.**
• ***** ***-*********-***: **.***.*******.*.*, ***** Date: ****-**-**
• ****** ***-******: *.**.**
• ********* ***-**********-*: **.*.** ***** 180316
• ******* *****: *** *****