Computer Vision Meetup March: How we built snapscreen

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Talk from Thomas Willomitzer at the Computer Vision Meetup March Edition about "How we built snapscreen"

How we built Snapscreen
From idea to scalable system

Initial idea
• Watched Superbowl game in Silicon Valley in 2013 and saw Shazam
enabled commercial on TV
• Worked at Jumio as CTO at the time having lots of exposure to
computer vision
• Added 1+1 to come up with the idea for Snapscreen
• Initial idea was to take a photo with any phone and send photo to
email address. In reply email send contextual information about
program you are watching

First steps
• Used OpenCV to implement MVP to proof the idea can work
• Did some homework on precision and scale metrics
• Did some homework on cost both in production of technology and
operation of technology for thousands of TV stations and millions of
users
• Seemed doable – so we went ahead

The first year
• Decomposed the problem into smaller problems: screen detection,
image matching, TV data gathering and storing
• Building of data sets for development and test of algorithms for each
smaller problem
• Solving of each of the smaller problems

The technology behind Snapscreen
• CI – nearly CD – built on Jenkins, Artifactory, Chef and Docker
• Highly scalable backend built on Java using state of the art Spring
Cloud
• Scaling built on Digital Ocean and Zabbix
• Persistence layers built on top of Postgres with CitusDB and
MongoDB
• Computer Vision layers built with C++, Neon, AVX2. All driven by
CMake and CTest

The product offering
• The vision is to automate the search for anything on TV
• For TV Apps this is identifying the program and deliver SERPs (search
engine result pages) for program title, actor names, episode
information etc …
• For Betting Apps this is finding the right game and bet immediately –
keep the impuls
• For Sports Apps – It’s a hybrid between TV Apps and Betting Apps.
We find the right game in the app – and also deliver SERPs for Teams
and Players

Lets see Snapscreen in action
• Show video https://www.snapscreen.com/intro-video
• Show live demo (and hope it works)
• Assuming the demo worked – who wants to try?

Computer Vision Meetup March: How we built snapscreen

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Computer Vision Meetup March: How we built snapscreen

1. How we built Snapscreen From idea to scalable system

2. Initial idea • Watched Superbowl game in Silicon Valley in 2013 and saw Shazam enabled commercial on TV • Worked at Jumio as CTO at the time having lots of exposure to computer vision • Added 1+1 to come up with the idea for Snapscreen • Initial idea was to take a photo with any phone and send photo to email address. In reply email send contextual information about program you are watching

3. First steps • Used OpenCV to implement MVP to proof the idea can work • Did some homework on precision and scale metrics • Did some homework on cost both in production of technology and operation of technology for thousands of TV stations and millions of users • Seemed doable – so we went ahead

4. The first year • Decomposed the problem into smaller problems: screen detection, image matching, TV data gathering and storing • Building of data sets for development and test of algorithms for each smaller problem • Solving of each of the smaller problems

5. The technology behind Snapscreen • CI – nearly CD – built on Jenkins, Artifactory, Chef and Docker • Highly scalable backend built on Java using state of the art Spring Cloud • Scaling built on Digital Ocean and Zabbix • Persistence layers built on top of Postgres with CitusDB and MongoDB • Computer Vision layers built with C++, Neon, AVX2. All driven by CMake and CTest

8. The product offering • The vision is to automate the search for anything on TV • For TV Apps this is identifying the program and deliver SERPs (search engine result pages) for program title, actor names, episode information etc … • For Betting Apps this is finding the right game and bet immediately – keep the impuls • For Sports Apps – It’s a hybrid between TV Apps and Betting Apps. We find the right game in the app – and also deliver SERPs for Teams and Players

9. Lets see Snapscreen in action • Show video https://www.snapscreen.com/intro-video • Show live demo (and hope it works) • Assuming the demo worked – who wants to try?

Computer Vision Meetup March: How we built snapscreen

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