Video Analytics Person / Face / Vehicle Guide

By IPVM Team, Published Feb 23, 2021, 10:17am EST

Person, face, and vehicle detection are the most commonly offered video analytics, but understanding how they work and what challenges can break them is not easy.

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In this guide we examine:

  • How to tell how well an analytics provider's person, face, and vehicle detection work.
  • The different pixel density requirements for person, face, and vehicle detection.
  • Person Detection performance and most common accuracy problems
  • Face Detection: performance and most common accuracy problems
  • Face detection vs facial recognition
  • Vehicle Detection: performance and most common accuracy problems

This is part of our new Video Analytics Course starting in March.

Different ********** ** ******, ****, *** ******* *********

********* ******, *****, *** vehicles *** ** ******** by, ********* ******* ******** and **** ********. ******* learning-based ********* (***, ****) is **** ****** ** IP *******, ******* ** is ****** ** *******, critical *** ****-**** ******, and ******** **** ********** than **** ******** *******. However, ******* ******** ** often ************* **** ******** than **** ******** (******* in**** **** ********* *********** **-***** ****** ********* Tested), ************ **** *********** field ** ***** *** lighting ****** **** ***** surveillance *****.

Performance ****** *** ********* *********

***** ********* *** ** specification ****** ** ***** that *** ** **** to ********* *** **** products ****. ***** ************* often ****** ******** (*.*., up ** **%, **%, etc.), *** ******* ***** they ********* ** *** match ****** ******* ***********. The **** *** ** find *** ******** ******** is ** ******* ** specific ********** (*.*., **** IPVM ****).

*** ********* ****** **** may ******* ******* ** what *** ** ****, but **** ************* ** not ********* ** ***** this ***********. ********, ** can ** ********* ** determine,***** ********* *********** / breaking *** ******, *** **** ** one **** *** *** that ***** ** ************ since ********* ********** ** accuracy ***** ******.

** **** ******* *****, camera ************* ********* ****** cameras ** "*****" ** having "**" *********, **** for ****-***** ******* ** show *********** ******** ********. For ******* *******, ******* ****** ********* ******** 2020[**** ******* ** ** released ** ***** ****].

Pixel ******* (**********) ******** - ************ **** ******

********* ***** ******* ~*** narrower ***** ** **** than ********* ******. ******** can ** ******** ** half ****** *** ******.

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*** ********, **** * 1080p ******, ***** *** be ******** ** ** ~20' / ** ****, whereas ****** *** ** detected ** ** ***' / *** **** *** vehicles ** ** ***' / **** **** (******** no ************, **** ********, etc.)

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Person *********

****** ********* ** *** of *** **** ****** and ******** ***** *********, offering ****-**** ******** *** quicker **************.

********, ********* **** ** object ** * ****** (i.e. *** ** ******, vehicle, ** ****** *****) enables **** ******** ***** behavior ********* **** ********* or ********/********* *********:

*******, ***** *** ************* and ********** ********** *** detecting ******.

Person ********* **********

***** **** ****** ********* analytics *** ******** ** full ***** **** ***** details ** * *******, there *** ******* **** cause ******. **** ** these ******* *** ************ related ** ******* *******, due ** ************ ******** and ******* **********.

******* ** *** ** the **** ****** ********** for *********, ******** ********* snow *** **** ** people:

***** ******* ** ******** or ****** ****** (*.*. headlights ** * ****** vehicle) *** ******* ****** challenge *** ****** ********* analytics, ***** ***** ***** detections:

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******* ** ******* ********** in **** *******, **** people ********* ********* *** miss ********* * ****** running ******* *** ***** is ******** ** * low ***** **** (* - * ***) *** the ****** **** ** "see" *** ****** ****** as * ******:

Running-Subjects-Not-Detected

**** ****** ********* ********* are ********** ** ***** to ****** ****** **** are ******* (********, *******, etc.), ***** **** **** people ** **** *** crawling:

Crawling-Humans-Missed-Completely

**** ******* ** ****** detection ***********/********, ** *** analytic **** *** "***" the ****** ******, *** subject *** *** ** detected, ** ********* ** lost **** **** *** partially ****** ** ********:

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* **** ****** ******* with ***-********** *********, **** systems **** *** ******* and ****** **** ** a ******:

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Face *********

***** **** ********* ** relatively ******, ******* *** determining ** ** ****** in * ***** ** a **** ** **** challenging **** ******* * person ** ******* ******* a **** ** **** smaller. ********, **** ***** surveillance ******* (*.*., ** cameras, ****) **** ******* processing *****, ***** ***** more ********* *** ***** lower ******** *********.

*********** ****** ** * fundamental *******:

  • ***** ** *****: ***** it ** '****' ** detect * **** ******* directly ** *** ******, performance *** **** ************* depending ** *** * person ***** ***** **** (down, ****, *****, ***.)
  • ******** ** ***** - While ** ** '****' to ****** * **** looking ******** ** *** camera ** * ****-*** scene, *********** **** **** significantly ********* ** *** lighting ********** ** *** scene (*******, ********, *****, etc.).

******, ****-*** ***** *** easiest ** ******:

**** **** ****** *********, face ********* *********** ** also ************* ******** ** environmental *** ********** **********.

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

***** **** **** ********* analytics *** ******** **** direct ***** *** **** lighting, ******* ** ***** most ************ ******* *** installed, ***** *** ********** problems **** ***** ******; primarily ***** *** ********.

**** ******* *** ********* on ********, *** ******* walls/in ******* ** *****, resulting ** ******* **** images **** ****** *** top ** *** **** visible:

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** **** ******** * combination ** *** **** of *** ****, ** a **** *****, ******* of ***** ******* *** installed *** ****** ****** look ******** ** *******:

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******* *********** ******* *** face *********, ******* ** the **** ****** ********, is *** ** ****** lighting. ***** * ****** may ** ******* *******, low ***** ***** *** obscure *** **** **** blur, *****, *** *********:

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***** *** **** ********* are *** ***** *********** for **** *********, *******, can ***** ***-**** ******* to **** ***** **** with *** ****** *** full ********:

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*** **** ****'***** ********* ********.

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

* ****** ********* ** mistaking **** ********* **** facial ***********; **** ********* finds ***** *** *********** determines *** *** **** belongs **. ***** **** detection ** ***********, ** it **** ****** **** recognition. **** ********* ******* generally ***** *** ********* a ****, ****** **** identifying * ***-**** ****** as * ****. ****** recognition ******** *** ****** in *** ***** ****** being ********** **** ************* a *****.

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***** **** *** ********* analytics, ****** *********** ******** relies ** **** ********* performance, ** ****** *********** cannot ****** ***** * face ** ********.

Vehicle *********

********* **** ******** ***** and ***** ** **** is ** ******** *** alerting ** ************* ******* or ******** ******** ** person ********* **.

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******* ********* ** ************* easier **** ****** ** face ********* *** **** not ****** **** **** of *** **** **********.

Vehicle ********* **********

******** *** ***** ********** are **** **** ****** to ******* ***** ****** detection **** ***** ****** or ****** ****** ******* detected ** ********:

Human-Detected-As-Vehicle

*** **** ****** ********* for ******* ********* ** that ******** *** ***** parked *** ***** ** a ****, *** **** analytics **** ******** ******** alerts *** *** **** vehicle, ***** ******* ****** a ******** *** **********:

Stationary Car Triggers Detected Constantly

** *** ****/**** *******, this ** * ****** problem.

Vehicle **************

******* **** **** ** vehicle (*.*. ***, *****, bus) *** ******** ** valuable *********** *** **************, and **** **-***** ******* detection ********* ******* ****:

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*******, *** ***** ** vehicles ********* ****, *** misidentification ** ****** *** SUVs, ********, *** *********** are ******.

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***** ******* **** ** relatively **** ** *********, based ** ******* ***** and ****, **** *** model ********* *** **** for ******* ************ ******* detection *********. **** *** more ****** **** **** learning ***/**** ********* *** require **** ****** ****** to ****** *** *** manufacturer *****/****.

Comments (7)

* *** ******* *** in **** ****** ** regards ** ****** **** analytics **** * **** seen *** **** **** 15 *****.

**** ** * ******** to ******* *********** ****** for ****** *** *** either *** ** *** industry ** ** *** have ********** **** ***** analytics.

**** ****, ***** *** certainly **** ****** **** have ******* ** *** past ** ***** - e.g., *********** ********* ** average ********, ******* ** video ********* ********* **** in ***** ** **** and ******** *********, ***.

* *** *** ***** about **** ** * Tutorial ** * ********** why ** *** ******.

* ***** *** ****** analytics ** ***** *** than **** * *** on **** *****. ******* it ** * **** teaching *****.

*-* ************** *** * product ****** ********** **** could ********* ****** ****** than **% ** **** I *** *****. **** could *********** *** **** truck, **** ** *********, coming ******** ** *** or **** **** *** camera, *** **** **** had ** **********, ***** was ******, *** **** were *** *** **** comapny **** **** **********. Another ******* ** ****** with **** ******* ( what ** ***** **** ago )

******* ( *** ******* out **** **** ** processors ****** ***** *******, had ********* ****** * camera **** ***** ********* men ** *****, *** in ********, *** ** uniforms, *****, **** ******** in ***** **** ****** at ********, *** *** common ********* *** **** dual *********, ****** **** behind *** ******** **** a ***** ** ******** bases **** ******** ** South ****** , ** same ******, ******** ******** areas, ***... ( **** showed **** ***** ********** the ******* ** ** before **** *** ** LAX ).

*** ***** ******** *** long *** *** ******* left ** *** *** in *********,*** ** **** more ***** ********* **** do ** **** ***** with "*** *********" **** used ***** ********* ** military ****** ** ******* objects ***** ***** ** "bad" ******* ******* **** a ********** **** **** a *****,*** **** ******** at **** **** ** the **** ****** *** that *** **** **** 12 *****-** ***** ***.

**** **** **** ****** analytics ***** ********** ** cameras *** ****** *** managed ********* **** ***** scenarios, ********** ** *******, live ******** ********** ************, ect......So *** ********* ** tests ** **** ** real *****. ***** ********* of ***** ** *** analytics ***** ** **** world *****.

** **** *****, ********* are ***** ****** *** still *******, *** **** a **** *** ** go. **** ** ***** analytics * *** ** this *** *** ** good *** ** **** a ***** ***** ********.

**** *** **** * years, *** *** *** analytics **** ***** ***** change *******. **** **** we *** **** ** actionable ************ /********* ** CRM ** ***** ***. Way ***** ** ***** competiton. *** ********* ******** but ***** *********.

*** ******* ***** ********* company **** ******** ****** competed ******* *** ***** companies *********, **** **** time ***** **** **** though **** **** ****, patents, ******** ********

* ******** ******* ** a ****** ** *** imager *******. *** ****** their ********* ** *** in *** *** **** up *** **** ***** still ***** ***** *** bugs.

**** **** **** ***** first **** ** * company ******* **** * used **** **** ****** better **** *** ***** manufactuer ** *********.

** ******* ** ****, if ** ****** ** crawling ** * **** and ***** ** * certain ****** ** *****, or **** *** * hummingbird ***** **** ****** in ***** ** *** lens, **** ***** ********* on *** ****** ************ today *** **** * false *****.

** *****, ********** ** what ***** ******* ** this *******, * ***** it ** **** ** say **** ***** ********* and ******** ****** **** broadly **** ************* ******* over *** **** * years, ******* ** *** maturation ** **** ******** (including **** ************).

***** ******** *** **** written. ******!

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