>> You're not going to want to miss this episode of
the AI Show where we talk to
the 2021 OpenCV AI Competition champions,
the Cortic Tigers, make sure you tune in.
[MUSIC]
>> Hello and welcome to this episode of the AI Show,
we're talking to the
2021 OpenCV AI competition grand prize winner,
the Cortic Tigers, as well as Satya Mallick.
Why don't you-all introduce yourselves?
We'll start with you Satya.
>> I'm Satya Mallick, I'm the CEO of opencv.org,
but the highlight of this show is Cortic Tigers,
I'll let them introduce themselves.
>> Let's do it.
>> Thank you Satya. My name is Ye,
and I'm the CEO of Cortic Technology,
and I'm Team Leader of the Cortic Tigers.
>> Hi, my name is Michael,
I'm the CTO of Cortic Technology,
and I'm also one of the team members of Cortic Tigers.
>> Fantastic. Let's start with you Satya,
can you tell us a little bit about what
the OpenCV competition was?
>> OpenCV AI competition 2021
was the biggest spatial AI competition in the world,
and it was based on this device
called OAK-D OpenCV AI Kit with depth.
This can do neural inference that can run
neural networks on the device,
and do depth estimation in real time.
This was sponsored by Microsoft, and Intel,
we had about $400,000 in prize money,
including the devices,
and the cash price.
We divided the whole world into six regions because we thought
that regional problems are different than global problems.
Every region has its own problem,
and it's not fair for
these competitions to be completely together.
We had regional prices as well as one global grand prize,
and it was fascinating
the various problems people solved with this,
and the participation was also awesome.
There were like 1,400 submissions
for the first phase, and out of those,
we selected 250 teams for
the second phase and every team member of those 250 teams,
they received OpenCV AI Kit with depth,
and with that, they built awesome projects.
The final number of projects we got was about 127,
and they were awesome projects.
Cortic Tigers was great.
The second prize winner is for Neonatal Care,
using the camera looking at babies to
monitor their heart and their well-being.
Similarly, the third prize winner was somebody
with an autonomous wheelchair, things like that.
The submissions, they just blew our mind away.
It was so awesome.
>> You were saying, so that's?
>> Well, that was pretty much what I wanted to say.
Again, thanks to our sponsors, Microsoft,
and Intel for helping us do this.
>> That's really cool,
so a ton of submissions.
Let's go to Ye Lu, and Michael,
what was it that you built that nabbed the winning prize?
>> Well, thanks for having us here today, Seth.
We build an open-source software project
called the Cortex Edge Platform,
or CEP for short.
It's actually a software package that helps
students and hobbyists learn,
and experiment with AI,
with OP and OP LITE cameras.
My teammate Michael here will give
a brief overview of what CEP is.
>> The goal of CEP is to allow
both the beginners and advanced programmers
to start doing rapid AI prototyping.
For beginners with no or little programming experience,
they can start by using
a visual programming interface for filing the software.
We actually built this interface based
on the Microsoft MakeCode project.
We extended the project by adding computer vision,
speech recognition, speech generation,
natural language processing or what it control,
and smart home controlling read so that beginners can
start reading AI projects right away with these functions.
They can also quickly differ by a quick understanding of how
AI works by simply drag and drop programming box in the workspace.
As they learn more about AI and programming,
they may actually want more possibilities and
power to continue building their projects.
We also offer a way to convert those visual programs
into Python code so that they can
edit them in the IDEs such as VSCode,
or they can export them into
Jupyter Notebook so that they can do exploratory programming.
This way, they have a progressive learning path
by using our software.
In addition, we also pulled
FAN-C Python API so that they can do distributed AI computation.
The syntax of our API is actually very
simple so they can make use of multiple AI devices,
such as the OAK-D Lite to do very complex workflows.
Actually, we brought a video today to
show some of the use cases of our software.
>> All right, let me put that up right now.
>> Hi. My name is Ye.
>> My name is Michael.
>> My name is Jane.
>> We are team Cortic Tigers.
>> We want to democratize AI for
everyone and we want to do it with OAK-D.
>> We believe AI democratization starts with people.
We want AI to be accessible,
and affordable to everybody.
This is why the OAK-D is such a perfect fit for our project.
We are developing software to support the OAK-D to enable
people to learn and build using this incredible technology.
Anyone can get started easily.
It takes only a few minutes to write an AI program that
runs on the OAK-D using our visual programming interface.
[MUSIC]
You can even use it to build your own robot.
Here is our LEGO robot, Charlie.
You can teach Charlie to say hello and interact with you.
[MUSIC]
>> Hello, Michael.
>> Follow my face.
Ask Charlie to count reps for you while
you're exercising, [MUSIC]
or even get him to control your smart home.
But what if you want to control
more devices and need more computing power?
Our advanced Python API will take care of that for you.
You can easily distribute AI computation to
many devices on the network and render the combined result.
The OAK-D, together with our software tools,
will allow anyone to use AI in any way they want.
[MUSIC]
Whether you want to learn about AI,
build a project to help someone,
or maybe just have some fun,
we've got you covered.
[MUSIC]
>> Well, that was super cool.
I have 100 questions.
Well, 100, I'm exaggerating.
But can I ask a couple of questions to you two?
>> Of course.
>> Yeah.
>> The first is, did you use a MakeCode to implement?
Tell me about the MakeCode bit,
because that part is super impressive to me.
>> We started with MakeCode.
We thought that it's a very cool visual programming language,
and it's very extendable.
We just kept on asking custom AI blocks into
MakeCode and hosted the MakeCode project into our Raspberry Pi.
Everything is hosted on the web server,
on a $35 Raspberry Pi computer.
You can access and program everything through your web browser.
So you don't need to install anything.
It's just very convenient for us.
>> That's really cool. The second question is,
the OAK-D camera,
did the models run on that camera or did they
run on a Raspberry Pi, or they're in Charlie?
Tell me a little bit more about that architecture.
>> Perfect question.
All of the deep learning models,
they actually run on the cameras.
Really, it frees up the host processing power
for us to run some of the other business logics.
For example, some of the voice recognition
and natural language parts,
that's run on the Raspberry Pi computer.
But all the vision deep learning is run
on the camera. It's awesome.
>> Interesting. When you're moving those blocks around,
you're basically getting the information like
where is your face in the picture,
and that's being transmitted over,
and then you're doing the actual MakeCode stuff
with that. Am I getting this right?
>> You're absolutely right. Yes.
>> That's cool. Tell me about Charlie.
Because I saw Charlie was counting push-ups and stuff,
which Charlie would never do for me.
I did one sit-up this morning when my alarm went off.
Sometimes, I do two.
But tell me about Charlie itself.
Is Charlie like a prototype for
an in-home assistant or is it more of like,
we want to show you what's possible kind of thing?
>> Yeah. I guess Michael will take this question.
>> Yeah, sure.
We have actually Charlie here.
>> Hi, Charlie.
>> It's actually a LEGO robot kid.
We modified it so that we add a Raspberry Pi 4 in here,
we add as a host,
and you want to see the Software inside.
Then we have the OAK-D light that's acting as
his eyes so that he can see stuff and also have the depth sensing.
What we can do with the interface you just saw,
we can actually program it to do a lot of intelligent things.
In fact, we have been posting videos of how
Charlie works or how we program Charlie,
on our Twitter and LinkedIn.
If you follow them, you're going to see a lot
of interesting and fun cases of it.
>> I see. It's more of like,
you're just trying to show the possibility.
Now, is there a GitHub link where
people can go and take a look at this code?
>> Absolutely. You can go to GitHub,
our CEP GitHub repository,
and you can just go and follow
two or three simple instructions to get it
installed on your Raspberry Pi and
experience everything that you saw on the video for yourself.
>> That's really cool.
I'm going to bring you back to the stage, Satya.
This is really cool.
Are you doing any more things like this?
>> We are doing a ton. It started in 2020
when we did OpenCV Spatial AI competition,
that's when it started.
It was a much smaller competition.
But the winner of that competition,
they built a backpack for blind assistance.
People could wear this backpack and it would assist
them in navigating walkthroughs and things like that.
Because of that success,
we did OpenCV AI competition 2021,
and hopefully, in the future,
we will keep doing these competitions as well.
We reach out to the community for help.
When we did OpenCV AI Kit with Depth,
we actually reached out to the community
for assistance in building this device.
We did a Kickstarter campaign and raised $1.35
million with the help of 6,500 backers.
It was the biggest Kickstarter campaign of
all time for a smart camera.
Since then, based on the feedback we received from our users,
we came up with a new version.
It is called OAK-D-Lite.
It is a lighter version.
Its weight is less than 3W batteries.
If you look at it, it can fit right behind my fingers.
It's just as powerful as OAK-D, the same processor.
The depth camera has
a slightly lower resolution and things like that.
But this is a camera for the masses,
it's selling for $79 on our Kickstarter campaign,
which you can go to opencv.org/kickstarter.
This is a 4K camera which can run neural network on it for $79.
It's a depth camera where the depth estimation is done at
200 frames a second for $79, and it's so cute.
I would encourage people to check it out and back it.
It's also a really good gift for kids because with Cortic,
we are going to collaborate and bring it for kids.
We'll create material for kids.
My 11-year-old is very excited about this as well.
Our delivery is right around December.
Perfect Christmas gift.
I've ordered a pack of 10 for all my friends' kids.
>> If my kid is going to make robots that can see and do AI stuff,
I know I'm pretty excited about that.
Well, thank you so much for spending
some time with us, everybody.
We've been learning all about
the 2021 OpenCV AI competition winners their project.
It's been really cool. Thank you for being with us.
Thank you so much for watching and
hopefully, we'll see you next time.
Take care, everybody. [MUSIC]