Turing LPR And Advanced Vehicle Analytics Tested

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******'* *** *** ******* analytics *** ******* ********* in *** *******:

• ******** ***** ** ****** captured (*** **** ****** were ******, *** *****) at **-*****
• ******* **** **** ********** detected ** *** **** types (******, ***, *****/*********, etc.)
• ******* **** ********** ******** (e.g., ****, *****, *****, etc.)
• ******* ***** ********** ******** during *** *** (*** mixed ******* ** *****, detailed *****)

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*******, ***** **** *********** weaknesses:

• ****** **** ********* ****** despite **** *** *** shallow ****** (******* *-* times *** *** ** testing *** ** ****** of ******* ******)
• ****** **** ******* ********** could *** ** **** and **** ********** *******, without ********* ******* *** unstacked ******* ** *** plate
• ******* ***** *** **********, but *** ******, ******** as *** ** ***** when ***** ****** **** visible, ********** ** *** vehicle's ****** *****
• ****** ******** **** ********** re-detected *** ********, ******* searches **** ********* **** from ********** ********
• ********* **** ******** ** vehicles *** ******** ******** times ****** ******* (*.*., "Green ***** *****")

*******

******'* ******* ********* *** included ** ** ***** cost. *** ******* ********* to ***** ***** *** detecting ******** (*** ** in *** ******/***) **** these ******, **** ** additional ********* ********.

****** **** **** **** planned ** ***** ********* these ******** ******** ** 2022. **********, ****** ****** LPR ** * $*.** USD *** ***** *** camera ***-** ** ***, but ** *** ***** been *******.

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

******'* *** *********** *** weaker **** *********** ********* we **** ****** **** as******* ******,*** *******, ********* *** *******, ** ****** ********* missed ****** ******* ******* angles *** **** *** and *** *** **** plates **** ******* **********, issues *** ***** ** these ***** *********.

*******, ***** ******* **** and ***** ********* **** competitive **** ****** *** vehicle **** *** ********, an ******** ********* **** found ** **** ***** enterprise ********* **** ** Genetec, ***, *********.

Few *** ***** ***************

****** **** *** ******* recommendations *** *** *****, PPF, *****, *****, ***., for ***** *** *** vehicle *********, ** ******** to **** *** ********* who ******* ******** **********.

** ********* ****** **** support ***** ** ******* and *** **** *************** given **** *** "***** pixels" ** *** ******* plate *** *** *** "local ***** ** ****-***-**" for ******.

***** ** ****, ** tested ** * ***** street (~***** *** *****) and *** ** *** test *** ** * ~20° ***** ** *** street *** ~*** *** at *** ****** ** the *** ***** **** pass.

Smart ****** ****/******* *********

******* ********* *** *** are ********* **** ******** ***** ****** *******, *** ** ***** lower-cost********* ******.

** *** *** *** vehicle ******** *********, ********* must ** ******* ** the *** ********* ** the ****** ******. ***** enabling ****** *********, ****** will ****** ** *** Turing *** ** *** metadata ** **********.

Random ***** ***** ******

******* ***** *** *** during *******, **** ******** the ***** **** *** read, ******* ***** ******* visible *** **** *** in *****. *** *******, the *** ***** ** shown ****** ** * vehicle ********* *****, **** no *** ** ***** metadata.

****** *** *** **** any *************** ** ******* these ****** ****** ** give ******* *** **** may ** *********.

LPR ******** ** ** ******, ***** *** ****

***** **** ***** ****** captures, ****** *** ********** read ** ****** ** a ***** **** **** cars ****** ** ~**-** mph, *** *** *****.

**** ***** *** **** plates **** **** ********* on ******** *********** ** moving **** **** *** camera ****** *** ***.

Multiple ****** ** *** ***** **** *********

************, ******** ****** ** the ***** ************** **** accurately ****, ***** ****:

Stacked ********** *** ****

******** ***** ********* ****** with ******* ********** **** missed ********** **** ******* through *** *****. ****** did *** **** *** non-stacked ******* ** *** plate ** ***, ****** ignored **.

****** *** *** **** feedback ** **** *****.

Vehicle ********* *******

***-***** ********** **** ** the ** ****** ** the **** * **** mail *** **** ******* by ******. *** ********* only **** ***-***** ********** once ** ~* ***** of *******.

Car ****** ********* **********

** *** ******* ******* color *** ********* ********** day *** ***** **** in ***** **** **** some **********, ******** *****.

***** ******* ***** *** most ***** ******** ** night, *** ********* ************* vehicles ** *** **** their ***** ****** **** on ** *****, * common ******** ***** ** vehicle *********. *** *******, the ***** ***** ** identified ** *** ***** its ***** ****** *** on, *** ********* ********** as **** **** ***:

Vehicle **** **** ********

****** *******, ******'* ********* accurately ******** *** ********* vehicle *****:

** *** *******, ***** classifications **** ******** ** all ******** ****, **** no ******************.

Vehicle **** ***** *** ********

******* **** *** ***** not *****, **** ** the ******* *** ~**' from *** ****** ** ~120 *** **** * clear **** **** ** the *** ** *** below *****.

*******, **** ******* **** was *****, ** *** accurate.

**** **** ******* **** ID *** *** ******* a ***** **** ** the *******'* ****/*****. *** example, **** ***** ***** Durango *** *** ********** from *** **** ** a ********, ******* ***** visible.

**** ***** ***** *** best ******** ** *** vehicle ****, ****** *********:

** ***** *** ********** the ********. ****** ** committed ** ************ ********* our ******** *** **********. We ********* ********** ****** accuracy **** **** ******** OR ******** **** *** front ** **** **** greater *** **** * clear ****.

Spam ******* ****** ** ****** ****

****** ******* ********* ******** from ***** ******* ** junk ****** ******* *** analytics ******* ******** *** every ******* ** *** scene ******** * ****** object ** ****. *** example, * ******* ****** across *** **** ** the ***** *** ** too *** *** ** produce ******** ***, *** every ***** ******* ** the ********** ** **-******** and ********.

****** **** **** ***** analytics ********* *** *** entire ***** ** ******* objects ** *** *****, not ****** ****** *******, but **** ****** ** optimize **** ** *** future.

*** ********* **** ******** currently ******** * ******** of *** ***** ** the ***** *** ********, since *** ******** ******** the ****** *****, ** is ******** *** *** algorithm ** ******** *** add **** *** ******* to *** ****** *******. We **** ******** ** look *** **** ** optimize *** **** ***** and **** ********* ***** to ******* *** *********.

******** ** ****** ******** is * *********** ******* in ******* ************* ** AI *********, ********* ** our****** ********* ******** ****.

Dumpster ************* ** *******

** *** *****, * dumpster ** *** ***** was ************* ** * green *** ******** *****, typically **** ******* ****** moved **** **.

****** **** **** *** not *********** **** ****** and ********* ********* ** from *** ********* ****:

** **** *** *********** this ******. ******* ******** zones ** ******* *** dumpster ** *** *** to ********* **** *****.

Versions ****

*** ********* ******** **** used ****** *******.

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