Uniview Deep Learning Camera Tested

By Ethan Ace and Rob Kilpatrick, Published on Jul 14, 2020

Uniview's intrusion analytics have performed poorly in our shootouts. Now, they have released their Prime Series Deep Learning cameras, with an "enhanced detection algorithm", but how well do these new models work?

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We bought and tested the IPC264SA-DZK, examining:

  • How accurate are person and vehicle classification?
  • Does it detect people when crawling, running, or obstructed?
  • Does it false alarm on common outdoor sources?
  • Does it ignore common indoor sources of false alarms?
  • How far can it detect people?
  • Does it integrate to third-party VMSes?

*******

***** *** ********-*** ****** missed ****** ** ******** in *** ***** *** did *** ******* ** small *******, ****, ** blowing *******, ** ******** from ***** ***** ****** similar ** ***** ** models ** **** ****** (such *******************), **** ***** ******* and *** ****** ************ detected *** ********** ** human, *** ********** ******** re-triggering ****** ********** *******, despite *** ******.

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

*******'* *********** ** ***** compared ** ********* ****** competitive ******** ****** **** Dahua *********+, ***, ** Hikvision ********.

  • *******'* **** ******** ****** performed ********* *******'* *********+, ***** **** ************ classified ***** ******* ** people *** *********** ** parked ********. *******, *******'* weak *** *********** *** classification ** ******* ***** as ****** **** ****** not ******* ** *********+.
  • *******'* ** ********* ****** than*****'* ***** ****** ********* (SMD), ***** ******* ** small/large ******* *** ******, with **** ***** ********* range (~** *** **. 11 ** *******).
  • *********'* ******** ******* *** NVR******** **** **** ***** alerts ***** *** *** trigger *** ******* ******, including ***** ******* *** shadows/reflections. ******* ** *******, AcuSense ********** *********** ** parked ********, *** *** not ****** ******* ***** as ******.

No ****** ******* / ****** *** ******** **********

** *** *******, ****** and ******** **** ********** detected, **** ** ****** objects (****** **** ************** issues, ********* *****). ***** subjects **** ********** ******** when *******, ********, *** running, ***** ****:

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Wheels ********** ** ******

****** *******, *** ******* AI ****** ********* ********* on *** ******, *********** them ** ******, ***** below:

**** ******** *** *** seen ** *** ***** person ********* ******** ** have ****** (****** ** issue ****** **** *** *******).

Large ******* ********** ** ******

*******, ***** ******* (**** as *** ***, *****) were ************ ********** ** people ** *** *****, triggering ***** ******.

*** ******** *** *** trigger ** ***** ******* in *** ***** ** view, **** ** ****, squirrels, *******, ** *****.

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

*******'* ** *** *** trigger ** ***** ****, throughout ***** ******** ****** in *** *******.

No ****** ** ******* / *******

** *** *****, *** Uniview ** ****** *** not ***** ** *******/****** foliage ** *******, *** most ****** ******* ****** of ***** ******.

No ****** ** ******* / ****** *******

*** ******* ** ****** did *** ***** ** common ******* ** ****** false ******, ******. *** example, ****** ******* **/*** and ****** ******* *** not ******* ***** ******, common ****** ** **** other ** ******* *** non-AI *********.

Person ********* ** ~** ***

** *** *****, *** Uniview ** ****** ******** humans ** ~** ***/~**' distance ***** * ~**-**° AoV. **** *** *********** is ******* ** *********** such ** **** ****** (*** *** ********) ********** **,***** ***/***,***** *********+, ************ ********/**********

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

** *** *******, *** IPC264SA-DZK's ******** ****** *** not ********* **** ***** party *****, ********* ********, Exacq, *******, *********, *** Network *****. ** ****** nor ******** ***** **** sent ** ***** *****.

Similar **** / ************ ** ******* *******

*** ********-*** ** ******* size *** ************ ** competitive ****** *******, **** as ***** *** *********, shown ****, **** *** using ***** ***** *** similar ******:

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*******

*** ********-*** *** ** estimated ****** ***** ** ~$300-325 ***, ******* ** Dahua's *** (*******,~$*** ***) ********* ********(**-*********-***,~$*** ***).

Uniview **** ******** ****** *** *********

*** ******* ********-*** ****** uses * ********* ***, seen *****.

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Versions ****

*** ********* ******** ******* was **** ** *** tests, *** ****** ** the **** ** *******:

  • ******* ********-***:*********-*************

******* ******* ** **** a *** ******** ******* is ********* ** ***********, but *** ********* ** the **** ** *******, which **** ******* **** improve ************** ***********. ** will ****** **** **** new ******** **** *********.

Comments (11)

******* ~$***, ********* ~$***, Dahua ~$***

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******* ********** ** *** best.

**** ** * ***** comment **** ****** ** purpose, *** **** * cynical ****.

*** ****** ** **** point ***** **** ** what *** ***** ** willing ** ***, *** many ******* **** **** way, *** **** *** Chinese.

**** **** **** *** because ***** * ********* OEM *** **** ***+ companies.

*** ****** ** **** point ***** **** ** what *** ***** ** willing ** ***, *** many ******* **** **** way, *** **** *** Chinese.

**** ****** **** * rational ***********: ****** ****** forces, *.*. *****/*************, ***** similar ******* ********.

****** * *** **** C.E.:

********** ********* ** *** process ** ***** ********* or ********* ******* ********* evolve ******* **** *****, coloration, ******, *** ***********.

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*****,

*********** *******, ** ******. Presumably **** ******'* ********* performance ****** ******* **** time. **** ** ******* itself? **** *** ************ set ************ ** **** of ***********, **** ** steady *****, ***.? **** AI ******** ***** * lot ** ***** *** is ******** *** *** all ****** ********* **** go ** ****.

**, ** **** *** retrain ******. ********* ***** actually ****-***** *** ****** rare.********'* **/** ****-******** ************ ******** *** **** well-known *******.

*** ******** ** **** "deep ********" ******* ***** from * ***-******* ****** network, ***** ** **** in ***********, *** ******* in *** ******(*) ******* firmware *******.

* ***** ***** **** you **** ** ** likely ** ******* "********" analytics **** ****. **'* getting **** *** **** common, *** ***** *** a ****** ** ******* with ****** *********** ** this ***** *****. ** it ** *** ***** of ***-********** *********? *** yet, *** ******** ** standard *** *********** *********, * **** ***********.

***** ******* ** ** many ******* *** **, analytics *** *** ************ of ***** **********. **** the ************* **** ******* out ** *** **** (IPVM) ** *** ****** and **** *** *** to ****** **** ******* updated ******** ** ******* algorithms ** ***** *** other *******? ** ** they **** *** **** there ** ** **** false *********.

******, ******* ************* **** us ******** *** ******* firmware. **'* ****** ****** a **** ****** ** IPVM **** **** **** we **** ************* *** preliminary ******* **** ********, they ******* **** **** variant ** (* ** only ******** ***** *********) "Oh, **, **, **'** get *** **** ******** now."

***, ** **** ***** what ******* **** *** give ********* ***********, ****** and ******* ******** *** free ** ****. *** need ** ** ** them *** ****** ** with * **** ***** tag **** **** ***** have ***** *** *******.

* **** *** ***** same ***** **** *******. no ****** *** **** examples *** **** * do ** ***** ********* ptz ** **** **** the ************** ** *** algorithm *** *** ** get **** ** *** it, *** **** *** my ******* *** *** theirs ** ****. ** go ** ** *****.

******* **** ** *** good ****

**** *** *** ** use? *********?

***, *** ********-*** **** a ********* ***, * added * ******* ** the ****** **** *** SoC ****.

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