Hikvision DeepinMind 2019 Test

By Rob Kilpatrick, Published Jun 06, 2019, 11:17am EDT (Research)

** ****, *********'* ********** ** *** performed ********, *********** ********, *******, *** ***** objects ** ******, ************** ************, ********** missing ****** ** *** ***** *** more ******.

free-images

***, ***** * **** ** ******** updates, ********* ****** *********** ************ ** human ********* *** **** ******** ********* performance.

** ****** *** ****** ******** ** the**********-**/*** ********** ***** ****** *** *** ******** ***** to *** *** ** ********, *********:

  • ***** ***** ***********: **** *** *** did *** ***** ***** ******?
  • ********* ********* ********: *** ** **** valid ****** ** ******** ** *** scene ** ******** **********?
  • **** ** *****: *** ********* ** time-consuming *** ***** *** ************?
  • **. *********** *********: *** *** ********** compare ** ********'* ****-******** ****** ******** cameras?

*******

***** ** * ***** ** ******* and ********* ********* ** ******, *** DeepinMind *** *** **** *********:

  • ** ****** **********:** ****** *******/******* ** ******** ******* through *** ***** **** ****** ****** a ***** ** ******* ** ******* conditions ********* ***, *****, *** ***** rain.
  • ** ****** ** *******:********** *** *** ***** ** *** shadows ** *** *****, ********* ******* wires, ********, ** ***** *******.
  • ** ****** ** ******* *****:******* *****/******** *** ******* ** ********** throughout *******, * ****** ****** ** false ****** ** ********* *********.

*******, **** ******** ***** ******** **** multiple ****** **** ** *** ******** tests ** **********, *********:

  • **** ** **** ******** ******:***** ******** ******* **** ****** ***** triggered ****** ****** ***** **** *****, light ** *****. ****** **** *** generated ** ********* **********, **** ********* on *** ****.
  • *********** ******* ******:*********** ** ****** *** ******* *****/********** on ***-****** ******** ***** ********* ***** positives.
  • ************* ****** *** ********:****** *******, ********** ************ ************* ****** as ******** *** **** *****.
  • *****/***** ******* ******* ****** *****:******* ****** ** ***** ****** *** small *** ***** ******* ******* ******* the *****, *** ****** ***** ******** be ********** ** * *****.

**** **** ** ********* ** ****** some ******** ** ********* ** ********* technical *******, **** ** ********* (******* time ****** ** ** *****) *** minimum *** ******* **** ********, *** neither ** ***** ******* ******** * material ****** ** ***** ******.

Vs. ********* ********* ********* / ****** ***

******* *** *********** ***** ***** ******, DeepinMind ***** *********** *********'* ******* **-******/*** intrusion ********* ********* (*** *** ****), ***** ********** ******* ** ******** objects, **** ** *****, *******, *******, light *******, *** **** ***** ** dust ** **** *****. ** ***** where ******* *** **** **** *** be ******, **** ** ******** ************, DeepinMind *** ******* ******** ********* ***** standard ********* *** ***.

************, ******** ** ******** ***, *********** is *********** ******, ** **** ****** side *** ***** ****** ***** ** any ***** ******, ********* *** *** limited ** *** ****** ***** ***** sources *****, ******* ************** ******* ******, vehicles, ** ***** *******.

Vs. ********* **********

** *** *****,*********'* ************** ******** ******** ****** ******* ****** false ***** *********** **** *** ********** NVR, ******** ****** **** **** ** addition ** ******* *** *****. ************, false ****** **** ***** ******* **** less ******** ** *** ********** ******.

**** *********** ********** ** ****** *** to ********** ****, ** *** ********** NVR **** * ******, ***** ********** deep ******** **** *** *** ********, while ********** ******* *** * **** powerful **** *** * ****** ******* of *****.

Vs. ******** ****-******** *********

** *** *****, ********** *** ********* triggered ** ***** ***** ******* ***** Avigilon's ** ****-******** ******** ******* ********. In *** *****, ** ******** ****** in *** **** ****** *** *** triggered ** *** ******* ****** ******* days ** *******, *** ***** *******, shadows, *******, ***.

Little ********** ** ****** ***********

** *** *****, *** ********** *** functioned ********* ********** ** **** ****** of ********* ****** *** **** *** testing. ***** **** ********* **** *, 4, *, *** * ****** ******, with *********** *********** ******* ******* ****.

Updated ******** ******

**** **** *** ********* ***** *** original ******* ** ********** *** ********. However, ******* ** ***** *******, ********* has ******** ******* ********** ** *** ********** *** series(***** *** **** **** ******* **** a * ******).

IPVM Image

** ***** ********* ***** *********** *********** between *** ***** *** ****** ***********, as **** ** ************ ** * models, ** ***** **** *********:

********* *** *** *** **********, ** see ******* ******** ******* ** ***** of ******** **** *** ********** ****** classification ****** ****** **** ***** ******...*** 2nd ********** *******, ** **** ** phase **** ** ****** ** *********.

Simple *************

************* ** ********** ** ******, **** intrusion ***** ** ** ******* ** the *** ****** ***** ******** *** "Enable ***** ***** ********" ******* ** the ********* ******* ** *** ***.

IPVM Image

***** ******** **** ** ********* (**** of ****** ** *****) *** *********** can ** ******* *** *** *** make ** ****** ** ********* ** our *****.

Rejected ***** ****** ** ***** *** *******

********** *** *** ***** ** ******* brush/trees ** *** ***** ** **** during *******, **** ** *** ***** on *** **** *** ***** *****, one ** *** **** ****** ***** alarms ** ******** ********* *****. ************, shadows **** *****, *****, ** ***** objects **** ********, * ****** ****** which ********* ***** ********* ** **** tests. **** *** ***** ***** ******* in *** ****.

No-Alarms-On-Blowing-Branches,-Foliage,-or-Shadows

False ****** ** ****

********** *** ******* ****** **** **** on ******** ** *** **** *** heavy ****, ** ***** ************ ****-******** *********,***** ***, *** ************** **************** *** *** ****.

Rain-On-Dome-Causes-False-Alerts

** ******** ** **** ** *** dome, *********** ** ******* ** *** ground **** ***** **** ***** ***** false ******.

IPVM Image

False ****** ** ***********

*** ********** ****** **** *** ****** false ****** ** *********** ** ******* of ****.

Reflections-In-Nonmoving-Vehicles-Cause-False-Alerts

False ****** ** *******

*******, ** *** *****, ***** ******* were ******** ** ****** ** ******** scenes. *** *******, *** *** ***** triggers *********, ********* ** * ******.

Large-Animals-Detected-As-Human

************, ***** ******* ********* ********* ******. For *******, *** **** ***** *** detected ** ***** ******** ***** ** our ****.

Small-Animals-Detected-As-Human

Alerts ** ***** *******

*******, ***** ****** **** ********* ** light ****** ** *** ***** ******* changing, **** ** *** *** ****** behind ******, ******* *** ***** ** darken, *****.

Passing-Cloud-Trigger-False-Alert-Due-To-Light-Change

Vehicles ******** ** ****** / **** *****

****** *** ***** ** ****** **********, multiple ********** ** ****** ********** ** vehicles ** ******** ********** ** ****** were ****.

*** *******, *** ****** ******* ******* the ***** ***** ** ********** ** a *******. *** ******'* ********* ********** can ** **** ** *** **** of *** ******** ***, **** ***** than *** ****** *******.

Human-Detected-As-Vehicle

*********, *** ***** ***** *** ************* as * ****** ** *** ***, though *** ****** ***** *** **** is ******* ********* **** * ******.

Vehicle-Detected-As-Human

No ****** ****** ** ******* ******

****** *** *****, ** *** ** instances ** ********** ******* ****** ******* or ******* ******* *** *****, *** did ** **** ******** ******* ******* the **** ** ********.

No-Missed-Person-Alerts-During-Day

*** *** *** *** **** ******* even ******* ** ***** ****, *****:

No-Missed-Person-Alerts-During-Night_Heavy-Rain

Demographic **** *******

******** ********/**** ******** ******* *********** **** about *** ****** ********, **** ** age, ******, *** ******* **** *** wearing * ******** ** *******.

*******, **** *********** ** ** ****** accessible ***** ******* ******** (**.*.** ***** 181028) *** **** ******** (**.*.*.*).

IPVM Image

Version ****

*** ********* ******** **** **** ****** testing:

  • ********* ***-*******-**/***: **.*.** ***** ******

Comments (9)

Great review!  I hope you all will get a chance to test out their new bi spectrum thermal with those analytics.  I will be real curious how the thermal imaging analytics work in conjunction with the optical.

Great report.

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Gary, thanks for bringing that to my attention, the link should be working now.

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Previous firmware/iVMS versions offered demographic data about the people detected, such as age, gender, and whether they are wearing a backpack or glasses.

too bad, it might have been interesting to see the demographic data it came up with on the crow and the truck...

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I'm yet to see a positive review about the DeepinMind range of products, which is a shame really when this is supposed to be Hikvision's answer to false alert minimisation. The tests above might as-well have been carried out on their entry range camera and NVR with the amount of false alerts triggered!

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Ahmet, just to clarify, as we mentioned in the report, DeepinView camera did better than Deepinmind and DeepinMind still performed much better than the conventional camera /NVR analytics.

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Sure, but when you look at a product like Netatmo for example, the ability to detect a human, car or animal or accurate every single time. It puts the likes of large 'CCTV' manufacturers to shame, hence my disappointment.

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We found that to generally be the case - i.e., 'consumer' companies beating 'CCTV' manufacturers - Consumer IP Camera Analytics Shootout - Arlo, Google / Nest, Amazon / Ring, Hikvision / Ezviz

We only tested Netatmo once and that was 3 years ago, so I cannot speak for them. Generally, though, consumer cameras with cloud connectivity have the advantage of doing cloud false alarm filtering which has helped them (i.e., sent a potential alarm to the cloud first and run a check / deep learning to validate).

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Thanks for that, very informative.

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