*******
*****'* ****** / ************** capabilities *** *** ****** anything ** **** **** seen ** ***** ************. This **** *** **** it ** *** '****' (as ********* *** ** done ** **** **** including *** ****, ** license *****, ** **** matching, ** ****** **********, etc. ***** ***** **** not **). *******, *** Google **** **** ****** input *** *** ****** detailed ******** ** ******, colors, ****, ******* ***** and ************* ******* **** lets ** ** ****** not ******** **** ** enterprise *****.
*******, *****'* ********* ** home ***** ** ***********, since *** ******* **** users ** ******** ** get *** **** ******** and **** ****** ** pay *** $**.** *** month *** ****** *** these ******** *********. ** contrast, ** ***** *** Camio's ********** ** ****** attractive / ** ********* differentiator *** ** ********** video ************ ********.
Key ********
**** *** *** *** findings **** **** ****:
- *******, ****** ******** ****** **** in *** *****, ****** with **** ********* ** mislabeling *** **** ******* not ******* ** ***.
- ******** **** ******* ** most ******, **** ********* such ** ***, ******* ***, sport ******* *******, *****, minivan, ***. ***** ********** are ********* ********, ****** some ******* ** ***************** occurs, **** **** ******** labels **** ********.
- ***** ******** ** *** some ******* ******* ** human ********, **** ** bags ** "*******", *** these ****** **** ********** in *** *****.
- **** ****** *******, **** ** lawnmowers ** *****, **** not ******** ** *** labels ***** ***** *** learned. ***** **** ***** may ** ******* ** more ******** ****** *********.
- **** ****** **** *** practically ******, **** ** many ******* ***** ***** labeled "*****" ** "****", and ****** ***** ********** "ceiling." ***** ******* ****** in *** ****, *** are *** ****** ** be ****** ** *** to *** ***.
*** ******** ********* ** Camio (********, ******, *****, direction) *** *********** *********, reviewed ***** ******** **** *** *** ** ******** with *** *** **** advanced *************** ********* *****.
*******
*****'* *** ****, ******** *** ******** analytics, ** $**.** *** per ***** *** ******. Their ***** ***** ****, which **** *** ******** analytics/labeling, **** ***** ******/******, is $*.** *** *****.
***** **** *** ***** any **** ***** *** live ********* ** *** and ******* ****** *******, with ** ******* *** search, ** *********.
Camio ******* ******** ********** ********
*****'* ********* ******* ***** using ******* ******** **********, which ******* ****** ** objects ** *** ***** for ******** *** ******, in **** **** ****** than ******* *********. **** labeling ** *********, ********* completely ** *******, *** human *********.
*** *******, ***** **** analytics ********* * ******* entering * ***** ****, Camio applies **** ******** ******, such ** "***** ******* vehicle", "****", "***********", ***., not **** ** *** moving ******, *** **** to ********** ******* ** the *****, **** ** grass, *****, ** *******. These ****** *** ** searched *****, ** ****** on ******** ******* ****.
**** ***** ******* *****'* labeling *** *** ** may ** ********/****:
****** ********
***** ********* ******* ****** ** all ****** ******, *** and *****, ****** *** outdoor. ** *** ** instance ** ***-***** ******* being ******* ** ******.
Vehicle ***** **************
******* ** ****** *** generic **** "********", ***** may ****** *** ******** types ** ***, **** as "***** ******* *******" or "*******" ** "****** car." More ******** **********, **** as "******* ***** ******* vehicle" ** "**** **** car" *** **** ********.
** *** *****, ***** searches **** ********* ******** (***** below), ****** ***** **** on *** **** ** caution, *** ********* ********** an *** ** * minivan, * ***** ** an ***, ***. ** found **** *** **** specific **********, **** ** "compact ***" ** "**** size ***" ** "******* sport ******* *******" **** less ******** **** *** more ******* *****.
Object *********
** ******** ** ******** objects, ***** ****** ***** to ****** ** ***** and ********* (***********/*********). ** users *** ****** *** "cars ***********" ** "****** wearing ****" (*****).
**** **** ***********/********* ***** to *******' ******** ******** ** *** camera, *** *** ******* motion ******** ** **** other. ** "****** *********** desk" ********* ******* ****** in ***** * ****** approaches *** ****** **** **** ** *******, not ****** ************ *********** a ****.
***** **** **** ****** to ******* *** ***** modifiers ******* ** *********** with **** ***** ** make ********* **** *********, and ****** *** **** of ********* **** ****.
Mislabeling ******* *******
** *** *****, ***** labeled ******* ******* ** human ******** **** * handful ** *****, **** of ***** **** ********.
*** *******, *** ******* carries * ********/***** ** the ***** *****, ***** was ******* ** "***" by *****. ****** ******* ** its ****/***** *** *** way ** ** *******, not ********.
***** **** ********* ** apply *** ******* ***** "weapon" ** * ******* carrying * ***** *****. Camio **** **** **** is ****** *** ** the ****/***** ** *** object *** *** *******'* posture, ****** ** **** similar ** * *****.
Objects *** *******
** ******* *********, ****** objects **** *** *******. For *******, ****** *** tests, ***** **** ***** clips ** *********** ****** grass, **** ** * riding ***** *** **** behind. ***** ******* ***** both ** "********", *** with ** **** ******** data, *.*., * "*********" tag.
Some Labels *** *********
*******, **** ** *** labels ******* ** ***** were ****** *** *********** useful ** **** *****. For ********, "****" *** one ** *** ********* labels, *** ********* ** simply ***** ***** ** which **** *** *******. Note **** **** ** not * ******** **** labeled ****, *** ************* tagged.
** *** ****** ******, many ***** **** ******* "ceiling", ****** *********** ***** clip *** ******* ******* in **, ** *** camera ** ** *******/******** mode ** * ********** small ****.
Camio ***
***** ********** ******** ******* setup ** ******* ******* to ***** *******, ********* by ********* ***** ***** or ********* **** *****, with ** ****** ** live *****.
*******, **** **** ********** this ******* **** *** "***** ***", * ***** **** factor ****** ***** ***** the ***** ******* *** cameras *** ******* ***** ** the ***** ******* ****** setup. There *** *** ****** of *** *********:
- ***:$** ******, ******* ** to ***** ** *******
- *** ***:$*** ******, ******* ** to ******* ** *******
Comments (16)
Undisclosed Manufacturer #1
I was just saying to my coworkers at ASIS about the next generation of video analytics being machine learning and using human feedback to "teach" the analytics, much like Google's Picassa or Apple's Photos does with faces.
The next day I came across Iron Yun at the rear of the show and was blown away by the capability and how it performed at identifying human subjects stacked deeply in the FOV using web cam video live.
I would be interested to see how the two solutions compare, and more importantly, I'm keeping my eyes open on this type of analytics since machine learning in general is finding its way into many aspects of our lives, like automated assistants. The security applications are going to be interesting...
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Undisclosed Manufacturer #2
That is a very interesting technology. I wonder how long until we see it integrated into a normal VMS, not as an add on like BriefCam or other analytic companies.
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Carter Maslan
Less than 1 year. I'm CEO of Camio and we're enabling cameras, NVRs and VMSs to embed this technology seamlessly with non-restrictive licenses, white-labeled services, and an extensible video processing pipeline. That way, manufacturers leap frog the low-level grunge-work to run Machine Learning at scale and at low cost without losing their ability to differentiate with unique video processing capabilities and service levels.
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Undisclosed Integrator #3
Very interesting product. Will keep my eye on this for sure. While many of our customers fear the cloud, I do believe something like this may start to tip the bar.
I do agree, quite heavily, that this is misplaced in the home market. If this were scaleable, and at the very least capable of processing in an Enterprise data center with only the metadata being sent to the cloud the potential for uptake in large enterprises is huge. I cannot tell you how many customer sites I see with one or two guards addressing elevator alerts, fire alarm alerts, door monitoring systems, card access alerts, key management, and answering random phone calls all while being expected to watch several hundred or thousands of cameras. Analytics alone isn't quite at the point it needs to be.
I can see placing something like HP Moonshot in a datacenter and sending the data out to the cloud being more acceptable.
I will definitely keep an eye on this company in the future. Thank you to IPVM for updating this as I missed the initial review.
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Undisclosed Integrator #4
the problem with home users is that they tend to have low cost equipments which do not produce metadata, and uploading 4-8 HD cameras to the cloud would take too much bandwidth.
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Undisclosed End User #5
I wonder if something like this could extend to internal loss prevention through sophisticated analysis of retail cash register transactions? Recognize your stock, serial register stream mismatches flag potential sweethearting, monitor movement of cash?
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Undisclosed
But is it safe in the cloud. sounds cool, vendor sounds like they have clue (see ISC West discussion article too.) What's in the cloud, how'd it get there, did getting the data there perforate the site network perimeter, is my data hanging out in the breeze not technically private and/or owned by me, etc. you know the drill...
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