For those of you interested in the fundamentals, check out our new class next week:
Tyler will be explaining fundamentals as part of our parallel effort to provide comprehensive training and education, in addition to our ongoing tests.
******'* *********** ******* *** ********* ************, that ** ********, ***** ** * significant ******* **** ************ ****** *********.
** **********,****** ******:
****** *********** *** *** ****** ** percent **** ***** – **** ***** have **** ********** ****** – ** images **** **** **** ** *** most *********** ********** ********* *******... ***** aspects ***** *******pose ********** caused by head movement and/or camera movements, ************ ** ********** ** ********** ******* (such ** ***** ******* ** ****, hair, ** ***** ** ******* ****** in *** **********),illumination ********** (such as low contrast and shadows), bright lighting that leads to washed out faces, low ******* and resolution that leads to noisy *** ****** *****, and distortion from cameras and lenses themselves. [emphasis added]
**** ********* * ******* ** ***+ 3-second ***** *****:
***** ***** *** **** ****** * number ** ****** ****-***** *********:
** ******** ******* ************ (**** ** 200 ***, **° **** ****, **° down **** ** * ***, **° side ****, **° **** ****, ***.).
** **** *** ***** ** ***** 100+ ***** ****** ****** ********* **********:
******, ** ******* **** ******* *** key ********* ** ***********.
Key ********
****** *********** ****** ********* ******** *** far ****** **** *** ****** ****** including ****** ** **** ** ********:
*** ****** *** ********* ***** '***' but **** ** *** ******** ** used **** ***** ****, ********* *** lightning *** *** ******.
*** *******, **** ** ******* *** scores ** *** ********** ****, **** for ****** ***********, ***** *********** ** performance ******* *** *******:
**** ******* *** *** ****** ****** lighting ***, **** '****' ***** *********** at ****** **% ***** *** ***** performance ** ***** **%.
***** *********** *** ********* ** ****** detection ********, ** ***, *** *** away, *** *******, ** *** ****** of ********* ****** ******** *** **** running ** ** **:
*********** ** **** ****** **** *** chart ******** ** *** *********** ***** service ******** *** ***** ** ** sent ***** ** *** ***** *** other ******** *** ** **** ******* / ** *** **** / ***** the ****** **. ** ******* ***** measurement ******* ** *** ***** ******* of **** ******.
********
*****, ** ******* ** ***% ******** for ****** ***********, ***** ** ****** strong *********** *** **** ** **° down *** **° **** **** **** the ******:
****, ** ******* ** **% ******** (meaning ~* *** ** ** ****** correctly ******* * ****) *** ****** Rekognition, ****** * ***** ********* ** steep **** *** **** **** **** the *******'* **** ****:
*** ***** ******, ** *****, **** much **** *********** **** *** ***********. For *******, **** * *** ** every * ****** *** * **** accurately ******** ****** **** * ***** angle *** *** ****** / ********:
*******, *** ** *** *** **** performances *** *********** *** ** **** test ********, ****** *** *****, *** subject ****** ******** ******* *** ****** but ***** **** *** *** ******** at ***:
Accuracy ***********
*** ******** ****** **** ** ************** and * **************. *********** **** ********* evaluation **** * ****** ******"**** ******* *********" (***), ***** ******** ***********-**-******* ** ****** face ********* ******** ************** ** ****** **** ********-*** ******* (something ****** "************ **** *****" ** IoU) *** ******* **** *** ********* captured * ****. ** ****'* ***** library, **** **** ****** *** *** face (***** ***** *** * ****, and ***** ** **** ***). ***** that, ** *** *** * ****** heuristic *** ********: ** *** **** frames *** * **** ******** (******** / *****-***** = ********). **** ** not *******: ** **********, ** **** not ******* *** ***** ********* (* face ** ******** ***********, **** ** the **** ** ** *** *******'* shirt). **** ******* ****** - ** we ***** **** *** *********. ** allows ** **** ******* **** ********** (lux, *****, ***) ******* ****** ** label ******** ***** *** ***** ***** of *** *******.
FPS ***********
*** ******* **** ****** **** *** Rekognition ***:
* **** ****** ******** *** ************ would ** ** ****** ****** ******** via*******->***********. **** ***** *** *** ********** overhead ** * *****-**-***** *****, ***** our ***** ***** *** ********** ******** from *** *** *********** ********. ** such, *** *** ***********, ******* **** low, ***** ** **** *****.
***** ** **** ************, **** ** a ***** ***** ******* ***********, ** others ** *** ** * *******. For *******, *** *** *** ***-***** hardware *** ****** ******* ***********. *** other ****** ** **** ******* *** on ****** * ** ***** ** devices (****-***) *******, ** **** *********** trade-off ** *********-*********. ************, *********** ** a ******* *******, ***** *********** * model (**** ********'* ****-******-****) ***** **** configuration *** *****. ** ***** ****-**** performance **** *********** ** ******** (****: we *** ***** ************* ** **** is ********), *** ***-****-**** ************, *********** could ** ***** ******.
*******
*** * ****-**** ***** ************ ***********,*********** ************ ** ***** **** *** *** other ************ ***** **** ********** ***** processing ** ******, ** ***** ** far **** ********** **** **-**** **********. For *******, *********** ******* *** **-**** (N. ********) ** $*.** *** * min ** **** ****** ***** ********. Even **** **** **% ******** ** analyze (*.*., *,*** ******* ** * month), *** ******* **** ***** ** ~$50 (*,*** * **% * $*.**) just *** *** **** *********.
** *** ***** ****,****** *********** ****** ******* ***************** ****** ***********, ****** ******** (***, gender, *******), ****** *** ********** *********, etc. **** **** ********* ******** ****** do *** **** ** ***** ** likely ** ******** ** ******* **** their ***********.
********
**** ** *** *** ** ****'* new ****** ** **** ******** *****, starting ********** ********* ******,***** ****** ******* ***** * / Movidius ** ******* *** ****. ** *** ******** a ******* ** *** ***** ** the ******** ******. *** ********, ******** or *********, ****** *** ** *** comments.
For those of you interested in the fundamentals, check out our new class next week:
Tyler will be explaining fundamentals as part of our parallel effort to provide comprehensive training and education, in addition to our ongoing tests.
Was any testing done running the algorithms with IR nightvision on? Most cameras would switch to IR nightvision (if they have the option) at 1 lux
We did not do any IR versions but we will try some in future tests.
Just wait until they integrate it with Ring if they haven't already.
Is there any way you can post all of your raw video used in the tests for IPVM users to download and run thru their own Analytics systems?
Even with just 10% activity to analyze (i.e., 4,380 minutes in a month), the monthly cost would be ~$50 (4,380 x 10% x $0.12) just for the face analytics.
Aren't you considering the "10%" factor twice in this calculation? 4,380 minutes itself is 10% of total minutes in a month.