Multi-Camera ****** ********
******'* ***** ***** ******* ** ************ ** "**-**************", the ******** ****** ******** terminology *** ******** *********** of a ****** ** ******** images. *** ***** ***** does *** ************* ** the ********* ** ******** for *** ** **** or ******** *****, ****** as-described, ** ***** ****** be **** ** ****** scenario, **** ****** ********.
Applications *** ******
****** ***** ***** ********* an "**********", ***** ** effectively * ****-**** *****, in ***** ** **** things "****-****", **** **** to ******* ****** ********, without *** ******* ********** of ***** **. ** a ****** ****** * disruption, ********** **** ****** from ******* ********** *********** (and ***** *** ********** for ***** *********) ** a **** ********. ***** able ** ****** * person ** **** **** from ******** *******, ******* of ******* ******** ******** guards **** *** ****, would ** ** *********** benefit. ****, **** ********** could **** ****** ******* their ******** ******* ** customers, *** ****-**** **********.
Overview ** ****** **-************** *********
****** ** ******** **** breaking ** ***** ** a ****** **** ******* sections (***** **** **** "patches"), *** ***** **** ***** to ***** ************ ***** algorithms ** * **** neural ******* ********* *** probability ** * *****. This ** *** ** looking *** ************ ** individual ******* ** *** image, ******* ** ******* only ** *** ***** image ** *** ******. If * ***** ***** is ***** ** ****** patches, *** ***** ** deemed ** ** ** the **** ****** ** the ******/******** *****. **** improves *********** **** *** person ** *** ****** seen **** *** **** angle, ****, ** ********/********** across ******** *******.
****** ******* **** ******** over *********** ******* ** relying ** ***** ** texture **** ******* **** data ** *** ****** reliably ******* (**** ** when *** ***** ** the ****** ** **** a **** ******** **** of ** **********).
Specifics ** "*****" ********
***** ******** ************* ****** ******** are ******* **** ********** of * ****** *****, in * ****-******** ********. The ***** (***** *) is ******* ***** * full-body ********** ** *** person, *** ****** (***** 1) ***** *** **** image, *** ****** **** horizontal ********, *** **** the ***** (***** *) slicing **** ** *** horizontal ***** **** ****/***** halves. **** ********** ***** ** ******** to ** * *****.
**** ********* ** ***** for *** **** ** patch ** **** ** fed. ****, *** *** Level * (****** *****) portion, **** ** *** 4 ******/******* ** *** individually ** *** **** algorithm ** ******* * separate ********. *** ******* **** distinct ***********, ** *** Level * ********* ******* at *** *** ***** ** images **** **** ** the *** ***** ** ***** images *** ********** ********.
* ********* **** ****** each ***** **** ******** sub-patches *** ******* ******** and deep ******* **********. **** stage ******* ** **** the ***-******* **** ******* of ******** ** *** image ******* ****, ***** the ***** ***** **** not ******* *********** ******.
*******, * *********** ****** learning ******* ** ******* out ** * ***** and ***** ***** ** the *******.
****** ****** ** **** 3-step ******** ** * Deep ******* ******* (***) method.
*********** **-************** **********
******** ** ******* ********** are ********* ** ******'* paper. ***** ******* **** of * ********** ****** of ****** ** * given ****** (***** ***** ideally ** ********* ** distinct ***** ******** ** the **** ******) ** train * ******* ****** network, *** ****** ** use ******* ******** ** "people" **** ****-****** ******* from ********** ******** *** differentiators ** * ***** person:
Claimed ******** ** ***** ********
** *** ********** ** the *****, ****** ****** that ** ********* *********** learning ********** ***** *** images *** ******** ** determine ********* ** ********* between **** (************** ******), **** *** *****-***** image ******** ********, **** can ******* ******** ******* for ******* ******* *********** of * ****** ****** multiple *******.
VIPeR ******* ****
** ******* ***** *** re-identification ****** ** *********** methods, ****** *********** **** the ***** *******. ***** ****** ** "Viewpoint ********* ********** ***********", which ** * ******* of ****** ** ****** captured **** ************ ******* having ********** ** ********/*******, poses, ******, ***. ** represent ******* **********. ******** of ***** ****** *** shown *****:
Performance ************ ** *********** *******
** ***** ***** **** to ******* ***** ******** to *********** **-************** *******, Disney ******** * *********** increase ** *********** **********.
*** **** **** ** the ***** ********** ***** the **** ******* ******* approach ** *** ***** 2 ******* (***** *** person's ***** ** ****** into * *******), **** overlapping ***-******* **** ********. The ***** **** ** using *** *** ********, but **** * *********** of * *********** ******* (color **********, ***/**********/***** ****, and ***** ***********), *** the *** **** ** the *** ******** ***** the ***** * ******* with ** *********** ****. The ***** *** **** lines *** *********** "**** crafted" ********** ***** ***** 2 ******* *** *** color ****:
********** **** ****** **** Disney ******** *** *** approach ****** **** * significant *********** ** **-************** accuracy, *** *** *** best ******* ***** ***** 2 *********** *******, ****** DSP ******* ** *********** input **** (***** ******) also ****** ***** ************ in **-**************.
Disney ** *******
****** ******** ******* ** IPVM ** ******* ******* of **** ********.
Impact ** ************ ********
******** ************* ***** **** ** sell ******, *** ***** large *********, ***** *** versions ** **-************** *********. ********, *******, *******, *** ***** ********* companies **** *** ******** heavily ** ******** **** ************ for ***** **** ******. However, ************ ****** ********, *** ***** **********, are ****** ** ****** to ******* ********** ********* ** a ***** **** ** developers. ** ****, **** allows ************* **** ****** to ***** ***** *** advanced ********* *********, ******* of ********** **** *********** suppliers.
***** ** ** ******** that ***** ***** ************ will **** ** ******* their *** **-************** *********, or ******* *********, *** possibly **** **** ***** do ** ***** ** harder *** ************* ** charge ******* ******. **** may **** ** **** affordable *********, ****** ***** technologies ********* ** * wider ******* ** *****.
Comments (4)
Igor Falomkin
Dear Brian, thank you for this article. I think that one of the most interesting parts of the article is the last figure. We may consider it as a current state of research in the field of reidentification. RANK 1 below 50% (and RANK 15 near 90%) may be interpreted as an indicator that the task of reidentification is not reasonably solved even on a database like VIPeR (it contains only 632 persons). So I suggest that on real sites (with thouthands of different persons per a day and hungreds of cameras) the method will not work feasible. Also it is interesting what about CPU (or GPU) consumption.
After this article from Disney Research it's interesting how these analytics work on real sites. Does someone have experience?
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Skip Cusack
Brian, thanks for a very interesting article. Interesting that Disney would "go it alone" instead of partner for technology. Makes me wonder if that reflects their desire to keep the details of this project as contained as possible (not supported by letting a technical paper), or if they just couldn't find suitable commercial technology.
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