********'******** ******** ********* *** ******* ****** Detection ***** ** "******* ****** **** *** go *********", *** *** **** **** claim ***** ** ** **** ***** scenes?
** ****** ******* ******** ********* *** Unusual ****** ********* *** **** * month, *********:
** **** **** ***** ** ****** unusual ******** ** ******? ** **** **** ******* ******** ** the ****? ** ****** *** ** *** ***** alert?
*** **** *** *** *** *** to *****? *** **** ** ******* ** *** identify ***** ******** ** ** ********* the ******** *******? *******
***** ** *** *******, ******* ******** Detection (***** ************'* ** ********* ) ********** ******* ** ******** ******** in *** ****** ******, ******* *** out, **** **** ********** ***** ****** due ** ***** ** ****** ***** detected ** **** ** *** ** detection ** ****** ******** ** *** tests.
*******, ******* ****** ********* (***** **** pixel *** **** *** ** *** most ******* ******** ******* ******* ****) suffered **** *********** ***** ****** ****** by *******, **********, *** ****** ***** turned ** *** *** ** *** testing, ****** ****** ** *********** ************ in ***** ****** *********, ***** ** is ***** **.
*******, ******* *** *** *** ****** users ** ****** **** ******** ****** as *** *******, ** ********** **** are ******* *** ********** (**** ** emptying ***** **** * ******** ******) will ******* ***/*** ******.
Avigilon ********
******** ******* **** ******** ** ***/*** performance:
***** *** *********, *** *** ***, are ******** ** ******* **** ******** events. ***** *** *** ****** **** an ******** ****** ****** ** ***** for *** ***** *** ***** ** “soft ******” ** ************* *** **** are ********* ** *** ***** ** Attention ********* (***). ** *** **** there ****** ***** ** * ****** in * ****** **** **** *** up ** ‘****** ** ****’ **** – ** ** ********** *** ******** and ***** **** ** **** *** false *********.
******** ******** *** ***** ** ********* interface ** *** ******* ***** ** deliver ******* ******. ***** ****** *** unanticipated *** ********* * ****/***** ** not ******* ** *******. ** ***** above ***** ****** **** ** ** more ******** *** *** ***** ** be ******** ******* ** ** ******** in *** *** *********. ***** *** the ***** ***** – *** ** providing **** **** ********* (*** *** ability *** **** ** ******* **** cameras).
Unusual ******** ********* **. ******* ****** *********
*** *** *** **** ******* ****, but *** **** *** **** ******** analytic **** ***** **********'* ** ********* (*.*., ***, *****, ****, ***************) *** determining **** ** *******, ***** *** uses ***** ****** ********* ******, ***** are ** ****** **** ********. ** our *******, ***** *****, *** *** significantly **** ******** **** ***.
******* ******** ********* ** ********* **** on *** ******** ******* . *** ** ********* ****** ,*** ,*** ,**** , ***** **** **** .
Accurate ******* ******** *********
******* ******** ********* ********** ********* ** our *****. ** *** ******* *****, it ********** *** ***** ** ******* (with * ****** ******** ***) ** they *** ****** ****** ** **** area.
*** **** ********* ** ******* *****. In ** **** ***** ***** ******** normally ****, ** ***** *** ******* subject ***** ** *******.
*******, ****** ******** ** ******* ***** of *** ***** **** ******* **. In *** ***** ***** *****, ******** frequently **** **** *** ****, ******, to *** ******* *** ** *** right, *** ****** ***** *** **** near *** ****** ****.
People ******** *** ********** ******* ********
***** *******/**** ***** *** ******** ********, people ******** ** **** ***** ******* the ***** **** *** ********** ** unusual.
Vehicle ********* ******** ***** ************** ***** ***** *** ******
** **** *********, ***** ** * truck **** ********** * *** ** an *** ***** *** **** ******* on ** ******* ********.
************, **** ****** *** ********** * person *** ******* ** ******* ******** event. **** ******** **** **** ****** several ***** ** *******.
Normal ******** ********* ********** *******
****** ******** **** ** * ***** driving ******* *** *** ****** *** day *** ******* **** ********* ********** unusual ****** *** ********** ******* *** that **** ** *** *** ** area ** *** *****.
Unusual ****** ***** ***********
******** ****** ** ** **** *** correctly ******* ** **** ** * person ****** ** * *** ******* area ** *** *****.
False *** ****** ** ******* *** *** **********
*******, ***** *** ****** ** **** accurate ***** ****** *********, ** ******** from ******** ***** ****** ****** ** everyday *********** *** **** ** ***.
*** *******, **** ********** ************ ***** of *** ***** ***** ** *** typically *** ****** ********* *** ******.
** *** ******* ***** **** ** these *****:
*******, ***** ***** ******* (*.*., ******* lights ** *** ***) ****** *** events.
Significant **** ****** ** ***** *****
**** ******* ******** *** ******* ****** need *********** ******* ** **** ** learn *** ***** *** ***** ****** accurate ******. *** ***** ***** * week, ***** *** *** ******* ** to *** *****. ****** *** ***** is ******** *******, ****** *********** **** as ******** ******* ******* *** ***** below *** ******* ***** ******:
******* ** ********'* ***** ****-******** *********, UAD/UMD **** * ******** *** ***** shows ******* ******** ********:
No ****** ******** ** *** ** ***
**** ******* ****** *** ******* ******** learn ******** ** *** ***** **** a *** ****** ** ****. ***** the ***** ** ******* *** **** will **** *** ****** ** **** both ********* **** *******, ***** ** no *** ** ***** *** ********* to *** ******* ****** ** **** it ********* ******* *** *** **** does ***, **** ** ****** ******** trash **** *** ******** ***** *****.
Focus ** ********* *********
***/*** ****** *** ********** ** ********'* Focus ** ********* *********, **** ****** highlighting *** ********** ******'* *** ***** blue. ********* *** ****** ***** ** view *** **** ***** ** ****** mouse **** ** **** **** *****.
** ***** **** *********** ******* ****** and ******** ********* ****** ** **** visibly ********* **** ******* * ********* event **** **** * *****-****** ****** because, ** ******* ** ******** **** ******* , *** ****** *****/******** *** *** organized ** * *** **** ******* the ******* ********* ** * ******** location. ********, ***** ******** ****** **** facial *********** *** *****-***** *** **** as ***/***, ***** ****** ** *******.
Simple ***** / ***
**** ****** ***** ** ******** *** UAD/UMD. ** *** *******, *** ** enabled *************. ** ******* ********** ***, users ****** ****** ** ** **** a ********.
****** *** ***** ** ********'* ***** Of ********* *********, *********** ** * lighter **** **** ******* ****** *** detected. ****** ** ******** ***** *** be ***** *** ***** ******, ******* to ***** ******** ******, ** *** be ********* ** *** ******** *** faster ******.
*******
******** *** *** *** *** ******** with ********* ** ******** ******* ******, no ********** **** *** ********.
******** ****
*** ********* ******** **** **** ****** testing.
******** ******* ******: *.**.*.** 
Comments (4)
Ryan Ritter
Happy to see our Spider Man detection is working!
In all seriousness, I am curious about the truck scene highlighted. Do you typically see trucks in your parking lot get that close to parking spots/drive across the end of the dock ramp in that manner?
That's my initial guess for what may have triggered it, especially as the alert stopped a couple seconds after that. Reminds me of our initial release that we showed at ISC West a couple years ago with UMD: triggered on a person (again, UMD, so this wasn't classified) walking in a park and dropped in and out, seemingly at random.
Turns out, the reason it triggered was snow covered the path and the camera remembered the areas of the path. So it would alert and drop accordingly as the person walked through the snow.
Great test. Enjoyed reading through it.
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