*******
** *** *****, ********'* ****-******** analytics worked **** **** **** properly **********, **** *** ** no ***** ******.
- ** ***** ****** **** common *******: ******* *****, ***** animals, ***** *******, *** shadows did *** ***** ***** alerts ****** *******, ******* or ***.
- ** ***** ****** **** rain: ******** ******* ****** from *** ********* ** rain, *-* ****** ******* from ***** ** *****, one ** *** **** common ******* ** ***** alarms ****** **** *********** analytics.
- ***** *** ******** ** well *** ******: ** *** *******, ******** ******** ******** human ******** ** ~*-** PPF, ************* ****** **** most ****** *********, **** as ******, *********, *** Dahua (**-** *** ** average), *** ******* ** slightly ***** **** **** Guard ***** (~**-** ***) and *****'* *********** ** (~10 ***).
******** *** **** *** shortcomings ******:
- ********* ******** ********* ***** * lux: ********* ******** ** ******** cameras ******* *********** ** low ***** ****** (lux), **** ~***' ******** at ***** ** *** 5MP *** ** ****** (7/10 ***, ************) ** ~55' (~**/** ***). ********* were **** ******* ** the *** *****, ******** to ~***', ********** *** required **** ~* ** 10.
- *********** ***** ******** *** best ***********: ***** **** *** ****** "self ********", ********'* ********* require ****** ******* ** ***** the ***** (****** ** vehicles). **** ***** ** static ****** **** ****** or ******** *** *** normally *******, ***** **** see **** *********** ** manually ******* ** ******* through *** ***.
- ******* *********** ****** ******** period:*********** ** ******* ** these ******* *** *** present ** *** ***** for *** ****** ** learn, **** ****** ** small ******* ****** ** *** testing ****** **** ******.
************
******** ****-******** ********* *** available ** **** **********'* *** ****** *******, ********* ***,****, ******, ***,*** ******(**/** ****), ***, *** thermal *******. ********* *** not ********* ** *** SL ******,** **** ****, ******* ********** ** *********** (****** **** *** ********** ***** ******** ********* multi ******).
Calibration / ****-******** *******
***** ********'* ********* *** self ********, **** ***** require ***** *********** ** order ** *********** ******* desired ******* (****** *** vehicles) *** ******** ******, such ** ***** *******. According ** ********, **** amounts ** ***** *** *********** ** *******, .


******** ************* *** *** require *** ****** *******/******* through *** ***** ** many ***** ***** ***** is ******* ********. *******, in **** ****** ******, such ** **********, **** fields, "** ***'* ****" areas, ***., ***** ******** are ****** ******* ***** normal *************, ******** *** take *****, **** *********** reduced ** *** ********.
*** *******, ***** ** calibration, *** ******* ******* a ********* *** * vehicle. ***** **** ******* calibration, ***** ******* **** reduced ** **********.

Solid ******* ***********
******** ********* ******* *********** was ************ ***** ********** testing, ******** *** ********* of ******* ***** / foliage ****** **** *** weeks ** *******, **** during **** **** ****, below.

********'* ********* **** ******* alarms **** ***** *******, another ****** ******* ** many *********** *********. **** that **** ******* ********* were ***** **** ** properly ********* (*** *****).

*******, ***** **** ***** analytics ********* ********** ** rain/droplets ** ****** *****, Avigilon *** *** ***** during *** ******* ** rain **** *** ****** of *******.

Indoor ***********
*******, *** ********* ******* false ****** **** ******* such ** ******* ***** changes, **** *****.

***** *******, **** ** light ******* ** ***** rooms *** ******** ******* on/off **** **** *******.

Vs. ***** ****** *********
** *** *****, ****-******** analytics ****** ************ ********'* standard ****** ***, ***** Avigilon ***** "***** ****** Detection", ***** ******* ** practically *** ******** ** the *****, ********* ******* cars, ***** *****/*******, ***. Sensitivity *** ********* *** be ******** ** ****** these ******, *** **** caused * ***** ******* to ** ********** ****** walking ******* *** ***** (shown *****).

Detection ******** ******* ***** * ***
******** ********* ************ ******* on *** ******* ** ~7-11 *** ****** *** day *** ********* *** drastically ******* ****** ***** testing ** ~**-** *** which ******** ** *** following ***** ** **** widths / *********:
- *.**-***-***-** (***/*:*): ~***' ****/~***' Distance ** ******** *** ~250' ****/~***' ******** ** night
- *.*-***-***-** (***/*:*): ~***' ****/~***' Distance ** ******** *** ~90' ****/~**' ******** ** night
- *.*-***-***-** (**/**:*): ~***' ****/~***' Distance ** ******** *** ~95' ****/~**' ******** ** night

********** **** ******* **** using **** ****** ** view, ****** ****** ** view, ******** ** ** and ***, **** ***** filtering ** *** ***, sensitivity *********, ***. ******** changes *** ** ********* effect ** **** ********* range.
** ******** ***** ********** with ******** ******** *********** who ********* **** **** either **** ********* ** brighter ****** (*.*. *** parking ****) ** ***** supplementary ********, **** ** external ** ************ ** additional ******* *****.
**** ******** ** ******** was *** ******* ** other ******* ******, ***** saw **** ****** ** moderate ********* ** ********, but *** ** ****** as ** **** ****. For *******, ****' ***** Suite ********* **** **** to ****** ** ************* the **** *** ****** *** *** night (************* **-** ***), as *** ***** *** (~10 ***).
Only ********* **** ***
********'* ********* *** ********* when ***** ***** *** Avigilon ******* ****** ***, which ** ******** *** event *************. ***** ** no *** ********* *** event ***** *** *** camera ** ** ****** in **** ***** ****** analytics. ************, ******** **** not ********** ********* ***** analytics **** *** ***** party *****, ****** **** point *** **** **** use *** ***** ***** framework, ***** ** ********* to ****** ** ******* these ******** ******. *******, ACC ***** ***** ** required *** ************* ** ***** cases, ****** ******** ********* and ***** ***** *** an ******** ***********.
Versions ****
*** ********* ******** **** used ****** *******:
- *.*-***-***-**: *.**.*.**(*****)
- *.**-***-***-**: *.**.*.**(*****)
- ******** ******* ******: *.*.*.*
Comments (27)
Itamar Kerbel
This report is very consistent with our findings.
At the moment Avigilon has the best Analytics system our there.
The only problems are price and 3rd party integration...
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Luca Fogliati
Great review really.It would be interesting also (i know it sounds funny)to "test" how the camera performs under spider web.It happens so frequently if the site is not supervised by 24/7 guard that some insect especially spiders can build their web on camera's glass.Especially in winter time and that might stay for hours untill somebody cleans it. That's the most challenging thing for analitics especially at night time with IR on and i've never seen a camera without millions of false alarms under that condition
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Fabian Muyawa
IPVMU Certified | 07/25/18 07:48am
Avigilon analytics appears to be reliable and the fact that it in compasses calibration/self learning features, this indeed is great tool for reducing false alarms. Also the outdoor and indoor performance in the above video tests are quite impressive as they are stable.
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Igor Falomkin
Thank you for this interesting report. A couple of questions:
- Did you compare CPU load of standard motion detector and this algorithm (during the self learning process and after it)?
- Does it require GPU or not?
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Murat Altuev
Great achievement, Avigilon!
Respect!
Such a good quality of tracker means very effective archive searching functions.
We need to check if integration with 3rd party software allows to transmit this tracks to server for further storing and analyzing during archive investigation.
Our typical problem is that server analytics is too havy for processing - means too expensive. If now cameras can provide such quality tracks, than problem solved!
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Undisclosed Manufacturer #2
I would like to see Avigilon amend its classification to "human", "vehicle" or "other" or something of the like. I run into a number of applications where alerting on "threats to infrastructure" are commonplace. Knowing that a family of rats, or groundhogs, which can damage property are also interesting to some customers.
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Undisclosed Manufacturer #5
Any idea about avigilon camera (/w self-learning capabilities) pricing ranges comparing to legacy VMD cameras?
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Murat Altuev
Interesting what ASIC they use, which allows deep learning classification on edge.
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Paul Steenkamp
Can somebody please clarify the distances. Is it Yards, Feet, Meters?
Thanks
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