******** ********** ************ **** ** "***** ******* ***** of ***** **** ****, ** ******* locate * ******** ****** ** ******* of ******** ****** ** ****** ****, or ******** *****." *** **** ** really **** **** ******** ** ******* search?

** **** ***, ** ****** ***** claims *** ******, *********:
- ** ** ****** **** ********* ******* footage ** * ****** *** ***** timeline ** ********* ******?
- ** ********* ** ******** ****** ********?
- *** ******** ** ** ** **** light, *** *****, *** **?
- **** ** ********** ******** *** *** gender?
- *** ******** ********** ********** *** ********?
Executive *******
***** ** *** *****, ********** ****** provides * ************* ****** ***** ** search *** * ******** ****** ** vehicle ****** ******** ******* ******** ** using ******** ** ********* ****** *****. However, ***********/******** *********** **** ** ***, gender, *** **** ***** **** ******* inaccurate ** *** ***** *** ********** IR (*** ********* ******) ****** ************* ** ******* *** appear *****/****.
Key ********
** *** *****, ********** ****** *** several *********:
- ****** **** ******* *** ******:***** ********** ****** *** ************* ****** than ********* ******* ********* ** ***** typical ********* ******, ********** ****** ******** cameras *** ******** ****.
- ******** ***** ****** *******:********* *** ***** ** ***** **** color *** ********* ******** ** *** tests ** ******* *** ******** *** light ******.
- ******** ******* **************:********** ****** ********** ********** *** ******** multiple ******* *****, ********* ****, ******, buses, *********** *** ********.
- ****** *************:** *** ********** ******, ***** ****** check ** *** ** ****** ** on **** ******. ** ******* ************* is ********.
- ** ********** **** **** ***:********** ****** ** ******** **** ******** Control ****** ********** ** ** ********** cost. ** ******* ********* ** ********.
*******, ***** **** ******* *********, ** well:
- ********** *********** ***********:********* ** ****** ** *** (*****/*****) were **********, **** **** ******** ***** as ****** *** **** ***** *** adults ***** ** ******** *** **** versa.
- **** ***** ** **** **** ******:**** ********* ***** ** ***** ****, subjects **** **** **** ********** ***** in ****** *******.
- ********** ** *** *******:** *** **** **** ** **, all ******* *********** ****** ***** ** gray, ******** *** ****** **** *** be ********. ******* ** ******** ******** in ** **** **** **** ******** than ***** ** ***** ******.
- ********** **** *******:** *** *****, ********** ****** ********* detected ********* ******* (***** ** *****) or **** **** ** * *******'* face **** ****** ** *** "**** Profile." ***** ******* ****** ** ******** adjusted ** ***********.
- ******** ********* ****:********** ****** ***** **** **** ********'* own *********, *** ***** *******, ************/********** ** *** ** ***** ********* appliances (* *********** ********** ****).
** *********** **** ** ***** **** and **** ** ********** ****** ** this *****:

Accurate ******* ******** ***** ******
** *** *****, ********* ***** *** lower **** ******** ****** *** ******** during *** *** *** ** ******** low ***** (~*-* ***). ******** ****** were **** ******** (***, ******, ******, blue) *** *****, ****, ***** **** accurate, ** ****.

Subject ****** ********* ********
********* * ****** ** * ******** subject **** ******** ***** *** ********* accurate ** *** *****, **** **** results ******* *** **** *******. *** example, ******* ***** **** *** ******* on *** ********* ******* ** ***** different *****.

Face ********* ******** ******** *** ********* **
********** ****** ******** **** ********* ** one ********* ** ****** ******. ** a ****** *** ******** * **** considered ******, ** ** ***** ** the *******'* ********** ***********, ***** *****. In *** *****, **** * **** was ********, ******** *** ********* ******, especially ****** ******** **** *** ******** changes. *** *******, *** ******* ** wearing ***** ********* ******/**** ** **** image:

*******, **** *** ** ********* *** users ** ***** ** ** *** to ******** ****** ***** ***** ***/*** not *****, ***** ** ** ********** of ******* * **** ** ** is *** ********, *** ***** ****** be ***** *** *****. **** **** is******** ** ** ********** ********* ** the *******'* **********, ******* ** ******** color, ******/***** *****, ***.
************, ***** ** ** *** ** mark * **** ** "*****" *** tell ********** ****** ** ******* **. This *** ** ** ***** ** the ******** ************ ******** ********* ******* such ** *** ***** (**** *****) or *****, ***** ****** ** ******* from *** ******* *******.

Searching ***/****** **********
**** ********* ***** ********, ********* ** age *** ********** **********, ******* ****** adults **** ********* *** ********.

************, ********* ** ****** *** **** inaccurate, ********* ****** **** ******* **** high ********* **** ***** **** ********.

Searching **** ***** **********
********* **** ***** ******** ***** *******, for ******* ********* ******* **** ***** hair ***** ******* ******* ** ****** wearing ***** ****, ********* *** ****** people ***** **** ******.

Accuracy ******* ** ** ******
********* *** ******** ** **** ****** with ********** ** ** ******** **** accurate ******* ** *** *******'* *****(*) can *** ** **********. *** *******, despite ***** ****** ** *** ******* in ** *****, **** ********** ******* of ****** *** *****.

Accurate ******* ****** *** ***** / ****
** ******** ** ******* ********, ******* color ****** **** ****** ****, **** red **** ***** ***** **** *** course ** ******* ****.

******* **** *** **** ********** *********, with ****** *** ***** ************** **** cars:

*** *********** ********* ********** ************** **** bicycles ** ***** ********:

Simple *****
** *** ** ********** ******, ********'* Analytics ******* **** ** ********** *** installed ** **** ****** (~* ****** process). ***** ************, ********** ****** *** be ******* ** ******* *******.

Pricing / *************
******** ********** ****** ** ******** ** ACC ********** ** ** ********** ****. Users **** **** ******** *** **********'* ******** ******** *** ** * ***** party **** ****** **** ********* *** (Nvidia ******* ** ******).
********** ****** ***** **** **** ******** Analytics, ****** ******* (*** *** ****)**** *********** ** ************ ***** ***** *******.
********
******** ******* ****** *.*.*.** *** **** in *******. **** ********* ** *** H4 ******** ******* **** **** ** testing, ********* ******, ****, ***, *** multi ******.
Comments (30)
John Honovich
Regarding the IR search problems, this is a general issue with IR and color based analytics, of course, but I think it could have a general impact on camera selection. The more people make use of such analytics, the more there will be a push for non-IR / low light cameras.
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Michael Miller
We have Appearance Search deployed on several large systems in many different scenarios with hundreds of cameras each and I am continuously impressed on how well it works.
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Daniel Tyrrell
I know when we tested AnyVision (Different product, but similar, I guess?) they claimed the system would only get better over time. As it sees more, and learns, especially with user input (yes this face is actually that person, no that is not a face, etc).
Do you think this is potentially true of Avigilon as well? I have some doubts as you mention you cannot tell the system that the picture of a puddle is not a face, or that his face is not the same person as the other one.
Does Avigilon plan on fixing that?
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Undisclosed End User #2
Just curious, what's a passing grade to earn a green checkmark vs. red 'x' on these tests?
For example, the motorcycle classification is called "generally accurately", which is fair, but I wonder if giving the green check on ~70% accuracy(7/24 images shown are clearly not motorcycles) is too generous. Of course you're probably looking at a larger data set in your eval, but it's tough to get that out of the report.
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Slava H
Thank you, great review.
It would be great to see some measurable test of Avigilon Self Learning, for example based on Gender or Age.
Also would be great to see how many results were missed vs found. For example Truck Classification - how many were found vs missed.
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Slava H
Yes Michael, it's very interesting.. That's what i want to see as a measurable statement (test results).
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Undisclosed Manufacturer #3
I suspect this is not measurable in the scientific sense of hypothesize then verify through experiment. There are too many variables which can impact the probability of accuracy.
In any real world scene, if you move the camera left or right or up or down the results of an experiment could be vastly different (or not) when it comes to analytics.
It seems to me the essence of video analytics is increasing the probability of detection of undesirable circumstances in the field of view of a camera in order to be more proactive in real time assessment and, in the case of appearance search, massively reducing investigation time even with the certainty of false positives. It's much easier to investigate something that might be wrong rather than investigate something you never would have known about without tireless hours of scrubbing video.
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Undisclosed Integrator #4
This is what I see as the future of camera systems and they’re ahead of the competition.
It’s a pity that it only works with their cameras and it’s expensive.
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