Bosch AI Camera Trainer Released And Tested

By: IPVM Team, Published on Apr 09, 2019

Bosch is releasing a highly unusual new AI feature - 'Camera Trainer'. Now, coming as a standard feature in Bosch IVA/EVA analytics, one can train the camera to detect custom objects one wants.

image

While detecting a face or a person or a car is increasingly common, Bosch is allowing users to define what objects are most interesting to them - whether it is a drill or a door opening or a ladder or a buggy or whatever might be important to one's camera scene.

On the positive side, this opens up many possibilities. On the negative side, it raises the question of how well this will work and how hard it is to set it up.

IPVM tested the beta version of Camera Trainer, configuring it in multiple scenes, to see how it performed.

*******

** *** *****, ***** Camera ******* ********* ********* as *********, ******** ** to ***** ** ******** objects ***** ***** *** be ************* ** ******** analytics (**** ** ****** being ******* ** ******* left ** * ****) as **** ** ******** on *** ***** ** some *******, **** ** doors ***** ****** ** closed.

*******, ***** *** ***** key ***********:

  • ***** ********* ** ***** subjects:****** ******** *** ***** would ***** ***** *** camera ** ****** ** object **** *** *** exist, **** ** * fold ** * ******'* clothes ********** ** * drill ** * ***** appearing ** ** **** door. ***** ****** ***** not ** ******** ** adding **** ******** *******.
  • ****/*********** **** ****** *******:****** ******* ******** **** object ***********, ****** *****, and **** ****** ******* to ******** ****. **** means **** ******* ******** depth ** *** ***** (moving ****** ** *******) or ******** ********* ****** in ****** ********* ** duplicate ********* ** **** cases. *******/****** ** *********** angled ******* **** ** trained **********.
  • ******* *** ***** ***********:** *** *****, ***** 1 *** *** *****, Camera ******* ****** ** detect ******* ** ***** otherwise ******** ***** **. This ***** ** ******** with ********** ***** (******* or **) ** *** scene, *** **** ** the ********* ****** ********* include ********** **.

****: **** ** * test ** * ********** version ** ****** *******. We **** ** ****** and ****** **** ****** if *********** *********** *** found **** ** **** Bosch's ******** **** ****** Flexidome, ******** ** *****/*** 2019.

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

****** ******* ** ******** to ** ********* ** the *** ** ***** 2019. ** ** ********* on ****, *, *** 7.3 ******, ***** ********:

  • ****** ** ********* ****/****
  • ********* ** ********* ****/**** IC
  • ********* ** ********* *** ultra ****/****
  • *** ** ********* *****
  • *** ** ****** ***** (color ******* ****, *** thermal)

App/Training ********

****** ******* ******** * few ****** **** *** user, ********** ***** *****'* Configuration ******* *******:

  • ******** *******: ***** ******* are ******** ** ******* a *** ****** * specific ****** ** ** trained, **** ** * drill, * ****** ****, a ******, ***. ***** is ** ******** ****** of ******* ******** *** training.
  • ******** *******: ***** ******* are ******** ****** ********, but ******* * *** around * ****** ***** the ****** ************, ** ** ******** on * ******** ***** positive *** ******* ** as ********.
  • ****** ** ********:  *** area ***** ********* ***** place. ** *** *****, ROI *** *** ******** or ******** ******** *** was ****** **** ** narrow ********* ****.

** ****** **** ******* in *** ***** *****:

HOG **. *** **************

***** ****** **** **** initial ******* ** ****** Trainer ** ***** *** (********* ** ******** *********) *** ****** ***********. However ** *** **** key ***********, ** ** cannot ********* *****, ******* drastically ** ***** ******** (e.g. *** *****) ****** where ***** ** ******* become **** *****, *** must ** ******* ** specific ****** ******, ******* of *******.

***** *** ****-**** ***** to ********* *********** ****** Networks (****) *** ********* in *** ******, ***** should ******* **** **** of ***** ******. ** the ***-****, **** **** plan ** ********* ***-******* models *** ****** ****** classes ** ****** ********.

*** **** ******* ** the *********** ******* ***** approaches, *** ********* ********* ************.

Camera ******* ***********

*****'* ************* ******** ******* regarding ****** ******* ***********, most ************* ****/***********:

****** ******* ********* *** limited ** *** ****. Due ** ***********, ******* appear **** ******* ** the ********** **** **** the ****** *** ***** much **** *****. **** difference ** **** *** pixel ******** ****** *** image ****** ** ******* with ****** *******. **** a ****** ******** ** restricted ********** *** ********* distance, ****** ******* ********* can ** ******* ** compensate *** ***-***** **********. Objects ** *** *** distance ****** ** ******** at ***, ** **** will *** ***** ****** pixel *** *** ****** Trainer ********.

*******, ******* ***** **************, we ***** ****** ******* performed ****** **** ******** with ****** ** ******** changes ** *********** *** size, **** **** *********** than *** ************ ********** Bosch **** ***. ** discuss ***** ****** *****.

Performance ********

**** ********* *****'* ************* guidelines, *** ****** ******* was ****** ******* ** recognize **** ** ***** and **** *** **. In *** ******* *****, the ****** *** ******* to ********* **** ******* cabinets **** ******, **** an ***** ** **** open *** ****. **** that *** ****** ** able ** *********** ******* the **** ******* *** person, ****** *** ******* is ****** ******** ****** him.

Cabinet-Open_Close-Status-Detected-Properly

** ******* **** ***** configuration, *** ****** *** trained **** ****** ** all ******** **** *** closed, ** **** ** negative ******* ** * human ** ***** ** open *** ****** *****.

Training Data (Positive And Negative)

***** *****'* ******* ***** configuration, *** ****** ******* was **** ******* ** count *** **** ******** were **** ****.

Camera Trainer Data Used For Counting

** ******* *******, *** camera *** ******* ** recognize ****** ** * pegboard ****, **** ** alarm ******* **** **** were *******.

*******, **** ***** *** have **** ******, **** other ******* ************ **** as ******, ********** ****** entering *** *****, *** also ***** ***** ** the ****. ** ****** of ********* ***** *** to *** ******* ******* these ***** ******.

Humans Trigger False Detection

**** **** ***** ****** cleared ***** *** ****** left *** *****, ** if *** ****** *** to ****** * ******* number ** *******, *** rule ***** ***** ********.

Perspective *** ****** **** ******

******** ******* **** ** roughly *** **** **** and *********** ** ***** for ****** ******* ** detect ****. *** ***** object ******** *** ******** is *** **** ****** must **, ** *** Trainer's ********* *** **** is ******. **** ***** that **** ****** ** detected **** **** *** side *** ****-**, *** example, ** **** ***** change *** ******'* ****** ratio **********.

******* ** ***** ******, objects **** **** ******** shapes *** ***** **** not ******* ****. *** example, ***** *** ****** could ** ****** ******* to ****** ******* ****** in *** ***** ** view (*****), ****** **** nearer ** ******* **** the ****** ** ******** their *********** ******** ** missed **********.

Perspective Angle Impacts Accuracy

**** ******* ** ****** a ******* ******, *** camera ******** ** ** two ******** ******* **** close ** *** ******, or *** ** ***, since ** *** **** larger **** *** ***** the ****** *** ******* on.

Duplicate Objects Detected At Close Distance

**** **** ***** ********** against ****** ********* **** different ************ *** ********* to * ****** ****** Trainer ****, ** ** may ****** *********** **** looking *** ******** *******. However, ** ********** **** may ** ******* *** trained **** ******** **** an ********** ***** ** distance.

Reduced *********** ** *** *****

****** ******* *********** ** low ***** ********** (~*.***) was *******, ***** ******* objects ** *** ******* to ***** ** **** it ***** ******** ***** on ** **** *****. This ***** ** ******** using ******** ************, *** no ********* ****** ******* integrated **.

Low-Light-Drastically-Reduces-Performance-(~0 5-Lux)

VMS ***********

***** ****** ******* ** integrated ****** ***** ******** analytics, **** ******** ***** used ** ******* ** standard **** ***** (****** detected, *********, ****** *******, etc.). ******* ** ****, Camera ******* ** *********** compatible **** *** *** that ************* *********, ********* *****, *******, and *********.

*******, **** **** ***** may *** ** *********. For *******, *** "*********" rule ** ********* ** "People ********" ** ******* Security ******, ****** ** does ********* ******* *** number ** ******* *******:

VMS Integration Same As IVA

Versions ****

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

  • ***** ************* *******:*.**.****.* - **** **** this ******* ** * pre-release ****, *** ********* for ****** ***. *** non-beta ******* ** ******** in *** **** *** weeks.
  • ***** ** ********* **** VR:*.**.****

Comments (11)

*********** *******. **** ****.

*** ************* ** ****** and ********** ****** ***** thereon ** ******** ********* that **** ** ******* as * ******* **** vendors (****** "*****") ******* in ************ **** *** customer.  ***** **** **** more ******* ********* ** it ** ********* ****** and *** ******* * superior *****.  ****** **** pay ******** ******* ********* to *** *** ****** models *** **** *** inherent ****** ** **** the **** ***********, ** will ** **** ********* for ***** ** ***** as * ******* *** customization ** ****** ***** where *****'* ********* ** the ***** ******* ** training *** **** ******** the ***** ***** ** the ********'* ******.  **** super ******* ** ******** running ** *** $***,**** (*** ******'****-* ** $***,*** *** DGX-2 ** $***,***) *** the **** *** ***** it ***** ** ****** good ******, ** ***** counterproductive ** ****** ********* to ********* **** ****** when ***** *** *** tools ** ********** *** job.  * ***** ** will ** **** ********* for ***** ** **** configuration ** * *******.

**** ** **** ** can ****** * ******* left ***** ** *** checkin **** ** ** airport.

*** ****** **** ****** analytic *** **** ** place **** ***** *** a *****. *** ****** trainer ******** ** ******** to ******* * **** customized ******** *** * customer. *** ******** ****** gives *** ******* ******** and ******** ******** ** what ** ** ***** trained ** **** ***. these ******** **** ******** from *** ***** *** the ******. *** **** examples ***** *** **** accurate *** ******** **. I **** ******* ** to ********* * ***** banner *** ****** ** min ** *** *********** the ****** *** ******** other ****** ** *** scene.

***** *** ****-**** ***** to ********* *********** ****** Networks (****) *** ********* in *** ******, ***** should ******* **** **** of ***** ******.

** ***** ********** **** for *********, **** **** effectively ******* **** ********* from ******** ** ********** since ******** **** ******** thousands ** ******* ****** images. *** ******* *** implementation ** ******** ** incredibly ****** ** ***** by * ********. *** it ** ******* **** as ******** ** ****.

** ***** ********* *** have ** ****** ** the ****** ** ** the ************* ******* ******** for *** ****** ******* tab ** **** ** in ************* *******?

* **** ********* ************* Manager *.** *** **** firmware *.** ***** *** same ****** **** ** one ** *** ****** above *** * ***'* get *** ***.

**** ** * **** to *** **** ***** on *** ***** *** Site:*********** ***** ********* *.**** ******** *** **** numbers *** *** ******** are ** *** **** sheet, *** ***** ** no ****** *** *** licenses.

**#*, *** *** ** camera ******* ******** *********** ** * *******, **** *** ******* is ********* *** ****** trainer *** **** ** available ** ************* *******.

*** ****** ******* ******* does *** **** * cost ********** **** **. It ** * **** license.

*** ********** **** ******** a **** ****** ******** about ** ** ** an **** **** ** have *** **** ******* released ** ****.

***** **** **** ** ensure *** ********** *** the ***** ******* ********* needed ** *** ** the ****** *******.

**** ** *** *** time * **** ***** of *** *** *** CNN ** - ** this * ******** *** all ****?
*** **** *** * guess *** *** ** the ****** ********? *** sure * ********** *** differences.
*** * ** ****** the ** ** ***.

** **** * **** primer ****** ****:**** **** ******** ********

*** *** **** *** both ******** ****** *** video *********. *** ** an ***** **********, ********* computer ****** *********, ******* a *** (************* ****** network) ** * **** learning ****** ***** ** what **** ****** ** video ************ ******** ******* are ***** **.

**** ******** ******* *** trained **** ******** *** learn **** ******* *** based ** **** ********. HOG/computer ****** ******* *** preprogrammed **** ********** ** what *****/****** * (******/***/****/***) looks ****.

** **** ****** ********* CNNs/hardware *********, *** **** had * **** ******** Class ******* ** ****:

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