********
**** ******** *** ****** a *** ** ********* in *** ******** ******** for *** ********* ** finally ******* ** *******-*** analytics ********. ** ** ********** to ** *** ****** evolution ** ********** ************ or ******* ******** *** ** being ******* ** * variety ** ********** ** help ********* ****** ********** and ******** ******* ****-***** criteria *** **********. *** the ******** ** **** report, ** *** ********** deep ******** ************ ** the ******* ** ***** surveillance, ****** ********* *****.
Traditional ***** ********* **********
***** ** **** ******** use ** *********, ******* typically ****** ** **** basic *********** ** **** defined ** ****** (*.*.: people *** * ******** aspect *****, ******** *** a **** ****** *****), and ****** *********** ** each ******. *** *********** step ***** ****** ***** the ****** *** ******* were, ** **** *** system ***** **** ************* ignore ******* ***** *** ground (*****, ******, ***** moving). *********** **** ****** define **** *******, ***** so **** ******* ***** be ******* ** **** were *** ***** ** too ***** *** ************ in * ***** ****.
***** ********* *** ***** ****** that *** ** *** wrong ***** ** *** image, ** *** ***** size, *** ********* ****** could ** ******** ** determine ** ** *** an ****** ** ********. In **** *****, ******* like ****** ***** ** color ********* ***** ** used ** ********* ** an ****** *** * person, ** *******, *** example. *******, ***** ******* typically *** *** **** a *** **** * woman ** * ********** from * *******, ** they *** *** ***** much ******** ** ****** details *** ******* ******* only ** ****** ***********.
***** ******* *** **** prone ** ****************** **** objects ** *** **** the ***-*** ************, * person ******** ** *** ground ******* ****** ******* clothing ***** ** ********** as * ******* ******* of * ******, ***** their ****** ***** *** uniform ********:

**** ******** ** ********* and *********** ******* ***** on ***-********* ****** ******** is ********* ******** ** as ******* ********.
Machine ******** ** **** ********
******* ******** *** **** learning *** *******, *** they ******** ********* ********** to * *******. ******* learning **** ***-********** ************ to ***** * ******** to ********* ** ***** of ** ******, **** learning ******* ***** ****** out *************.
******* ******** *** ** ****** how ** ********* * ***** walking ** *********** ********** such ** *** ***** of *** ****** ****** be ****** **** *** width, ***** ****** ** movement ** **** *** legs **** ** **********, it ****** **** ** a *********** ********* ******* of ********, ** ****** have **** ***** *** texture ******* (************ ********), and ** *****. **** the ********* ** **** fed *****, ** **** look *** ***** **********, and ** ** ***** enough ** ****, ** will ****** *** ***** contains * ****** *******.
** **** ********, *** software ** *** ****, and **** *** **** represents *********, **** ** a *****. ** **** breaks *** **** **** into ******* **********, *** looks *** ************ ****** all (** ****) ** the **** **** ** can *** ** ***** an ************* ** *** to ********* ****** ********* of *** **** *******.
*** **** ******** ********* should **** ** **** criteria ** *** *** that ** **** ******* to **** *** ******* learning ********* *** ******** programmed ****. ** **** cases, *** **** ******** algorithm **** ** **** further, ******** ****** **** humans *** *** **** thought ** ******** ******, or **** ***** **** been **** **** ********* to ******** ******, **** as **** *** ******** relationships ** ****** *************** in ******.
What ***** ** "****"?
**** ******** ***** **** the ****** **** ******* a ****** ** ************ classification ******, *********** *******, to ***** *********. * system ******* ** ******** vehicles ** ***** ***** **** learned ** ***** ** design ******** **** **********, taillights, ******** ** ****** or *******, ****** ******, and **** *****.
**** *** ****** ** vehicles, **** ****** ***** then ******* **** ******* multiple ****** (*********** ** the ****** **** ** the ***** *****) ******* for ******** ** *** learned ***** ******* ******* manufacturers, *** ****** * decision ** ***** ***** of ******* *** **** likely ** *** *****. In **** ****, "********" has *** ******* *******, as ** ******* *** most ********, **** *** system ****** ********** *** image ******** ******** ********** with "********" *** "*******" vehicles ** * ****** extent:

***********, **** * ******** layers *** ******** *** the ****** ** ** classified ** "****", ******* it ** *** ******** for ***** ** ** 10+ ****** ** ************** in **** ******** *******.
*** ** *** ******** ways **** ******** *** be ***********, *** ***** of *** ******** ******* is *** * **** indicator ** ******* ****** performance ** ***********. **** like *** ****** **** of * ****** ** not ** ******** *********** factor ** ********** ** image *******.
How **** ******** ***** *********
**** ******** *** * better ************* ** *** characteristics **** ****** ******* objects, ******* ** ******* on ************ ** ***** appearance. **** ***** ** better **** ** ******** objects ********** ** *********** conditions, ** **** *** object **** *** ***** any ***** *********** ** expectations.

********* ************ ********* ******* around ********* ** ****** at * **** ** place ***** ** ****** not ** (*.*.: *********** detection *****-*****). ** **** cases, ********* *** **** to ****** "********" ********, people ******* **** **** usually ****, ** * large ***** *** ****** in *** **** ********* suddenly (**** ** ** a ***** **********). ***** they **** ***** ****** is ** *** **** first *****, ********** ******* that * **** ** pixels ** * *****, and *** * *** (or * *****, ** a ****). ** ***** able ** ****** ********* objects ** ******** ** a *****, **** ******** helps ***** ********* ******** their ******** *** **********.
The **** ** ********
* **** ****** *******'* performance ** ***** ** the ******* *** ********* of ****** **** *** training. ******* * ****** that *** ******* ** recognize ****** **** ** showing ** ****** ** body ********, ** **********. That ****** ***** ****** fail ** ********* * majority ** ******** ******, because *** ******** **** was *** ****** ******. In ****, ** ***** perform ***** **** * manually ********** ****** ******* learning ******.
*******, **** *** ******** by ***** ***** **** with *********, ** **** of ********* ** *************** of *** ******* ******** for **************. ***** ******** images ****** ******* ***** of *** ****** **** multiple ******, *** *** cases **** ******, ** a ******* ** ***** and ********.
******* * ********** ** images, *** ******* **** with * ******** *** be **** **** *********. ******** ** *** ******** ********* used *** *** ********, as ** ******** ******** of ****** *********** **** thousands ** ********** *** sub-categories. ***"******" ************* ******** ****** *,*** sub-categories ** ******, **** classifications **** "*******" ** "executive".

***-***** ***** ********* **** the ********** ** **** much *****-******* ****** *************** than *******-******** *******. *** example, * *** *** be *** ****** ** firearms, ********** ** "**** automatic ******" *** "********" (as **** ** ******** other *************** **** *****, shotgun, ***.).

**** **** ********, ** would ** ********** ** expect *** ****** ** classify ******** ************, ***** on ***** ***************, ********* impractical *** *******-******** *******.

*********, ** ***** ***** a ****** ***** ****** of *** *** *****, young *** ***, ** build * *** ******* of *********** ****** *** age. ********* ** **** type *** ******** ******* in ****** ************ ** allow ********* ** ******* customer ******** ** ****** who **** ** **** advertisements ** ****** ********.
Gender *** ***
*** ******* ***********, ********* in ************ *****, *** in ****** ************ ** to ****** ******** *********** based ** ***** ****** and ***. **** *** uses ******** ****, ****** images ** *** *** women, *** *** *****, that ****** *** ******** to ****** ***** *********** seen ** ****** ** what **** **** **** trained **. ******** ***** include:

Filtering ****** **** **** ********
*** ********* **** ** growing ****** ***** ************ is ***** **** ******** as * '******'. *** benefit ** **** ******** every ***** **** * surveillance ****** ***** ** very ******** *********. ******* of ***** ****, *** system ***** *** *********** video ********* ***** ** narrow **** *** ********* objects ** *******, **** forwarding **** ****** ****** matches ** **** ******** to ****** ** *** object ******* ** ********, for *******, * ******, instead ** * ***, a *********, * ****** or * *****. *** example, ******* ********* **** ******** recorder ****, ** ***** **** example **** *********:

Training **** *** *********
**** * ********* ***********, there ** ** *** for * **** ** know *** *** ****** was *******, ************* ********* will *** ******* ******** used *** ********. ***** times *** **** **** in ******** ** ********** by *** ************ *** adapted *** ***-***** ** their *******, ********* ********* this **** ***** **** other ************* ** ********* in ***** **** ** re-use *** **** **** for ***** *** ******** purposes.
Potential ******** ****** *******
**** ** ******* ** training **** *** ** *********** when ******* *** ******* based ** ****-***** ******* that *** ***** ** the ********** ******, *** differ **** ***** *** product **** ** ********. Vehicles may **** **** ********* in ***** **. ****** or ** * ******** region **. ** ********** area, *** ***** **** not *** *** ****** on *********** ** ***** region ***** **** * system ** ******. ********, a ****** ***** ** trained **** ********* ***** or European ** ******* ********. Such * ****** ******** in ******* **** ****** of ********* ********, ******, etc. ***** **** ******.
Hardware ************
**** ******** ******** ******** resources *** *** *********** elements:
- ******** *** ****** ******* with ******
- ********* *** ****** ******* in * ******* **** a ****** ** ********
**** ** ***** ******** rely ** *** *** of ****, **** ** particularly ********* ** *** case ** *******/*********, ** they **** *** **** traditionally ***** **** ****, meaning **** *** ******** is ********* ******** *** DNN ********* *** ******** to **** **** **** a ******** *******.
******** ** **** *************** intensive, *** ********* **** specialized **** ******** *** this **** **** *** much **** ********, *** expensive, **** **** ** used ** * ****** or ********. ********* ** the ****** *** ********** of ****** **** *** training, **** ******* *** take *****, **** ** weeks ** ********, *** usually **** ** ******** GPUs ** ********. *** training ***** ******* * model **** *** *** DNN ** *** ** classify *******, **** ***** is ********* **** ***** relative ** *** **** of *** ***** ****. Nvidia ** * ****** supplier ** **** *** this ****-*********** ******** *****.
********* *** *** ** a ****** ** ******** uses * *****-***** *** to **** **** **** suitable ** **** *******. Here, *****-**** *** ** made ******* **** *** power ***********, *** *** number ** ******* **** can ** ******** *** classified ** *** *****, or *** **** ** takes *** ****** ** classify ** ******. ********* like *****/********, *** ******* **** ******* *********** **** targeted *** ****-***** ************. ****** **** *** ****-***** ********, but **** ******** ***** expansion ***** ******** *** PC-style ********.
Deep ******** ** ************ ********
*** **** ** **** ******** Video ************ ********* ********** ********* ** *** security ******** ******** ******** with **** ***** ** deep ******** ******** ** capabilities. ************ ***** **** facial *********** ** *********** masked *******, ** *********** entire ****** ** ****** Google-like ****** ************.
Evaluating **** ******** ********
** *** *** ********* to ****** * **** learning *******, **** ***** strongly ********* *** ******* be ****** ** * location *** *********** ** similar ** *** ******** deployment ** ********. ***** manufacturer ***** ***** **** best-case *********** *********, **** do *** ****** *** proper ************ *** ****-***** deployments. *******, ***** ***** be ********* **** * period ** *-* *****, giving *** ****** ********** time ** *** * variety ** *******, ******** performance ** ** ******** over ***/***** ********** *** across * ****** **** set **** * ***** lunch-time ******* *** ****.
Future ********* *** **** ********
********** ********, *** ***** we *** ** *** deep ******** *********** ** equivalent ** *** ***** days ** ** ******* and ******** *****. *** capabilities ********* ***** *** a ******* ***********, ** most *****, **** ******** options, *** *** ***** far **** *****. ************ are ***** ***** **** on ****, **** ********, and **** **** **** used *** ******** **** we ********** *** ******** available ***** ** **** fairly ***** **** ******** to ***** *********** ** the **** *-** *****.
***** **** ** ********* need *** ***** ********* that *** ** ********* with * **** ******** product *** ******* **** deploying * *********-********* ********. However, ***** **** * less ********* ****, ************ in ******** ***** **** search *** ******* *********, would ** **** ** exercise ******* ** ****** a ******** *** ***** in *** ********* ***** of **** ********.
Comments (26)
Bob McCarvill
Although it scares people, Deep Learning will only become a bigger and bigger market as we try to eliminate humans in the workforce. Cameras will be used to monitor assembly lines and also react when an event occurs. Similar technology can already be seen used in self-driving cars.
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Nathan Wheeler
Great tutorial. Thanks for providing it.
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Suresh Yendrapalli
Interesting insights differentiating machine vs deep learning.
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Skip Cusack
Hand coding basic constraints to my mind is not learning, it's an initial set of conditions that are then tested against video for compliance (to within a prescribed degree of agreement). If these initial conditions are defined by a person, and tested against video without any updating or modification, there is no learning. Machine learning, or any type of learning, needs to have a feedback (either positive or negative) to induce the learning.
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Undisclosed Integrator #2
This is quite a fun way of explaining the difference between the way CPU's work compared to GPU's
https://youtu.be/-P28LKWTzrI
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David Nguyen
We see this level of sophistication with technology in films, but it was surprising to learn that he have the concept under way in reality and will improve in the very near future.
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Jared Beagley
11/01/17 06:59pm
This is a very useful tutorial in helping explain some of the general concepts of machine learning, deep learning, and AI. We recently expanded our SkyHawk surveillance drives with models called SkyHawk AI so it's very intriguing to learn more about the specifics of what this all entails.
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