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Video and Image Analytics with Machine Learning

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JOACHIM HAGGER

FIAT/IFTA World Conference 2016 Warsaw

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About 4Quant

In the field of big image analytics, we disrupt the way videos and images are

analyzed and interpreted by leveraging artificial intelligence and big data

analytics on vast amounts of images. This allows for significantly better utilization

and makes searching, finding anomalies, and tagging much more efficient.

Our office is located in Zurich, Switzerland. 4Quant was established as ETH/PSI

spin-off in 2015 and has a core team of 5 big imaging experts.

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Technology

By utilizing streaming Big Data analytics we can process high-resolution at

incredible fast rates using commodity hardware. We have tested our system on

high-speed camera systems producing 8GB/s of rich image data.

Scalable hardware and a flexible software platform enable algorithms beyond

the simple standard vision problems of face detection and pedestrian tracking and

allow us to use Deep Neural Networks to extract complicated information from new,

high-resolution imaging sources like 4K drone video streams.

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Healthcare

Where is the tumor in this patient?

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Climate Change

How much is this lake shrinking?

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Surveillance

How many runners are in this

crowd?

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Autonomous Vehicles

Is this intersection safe to

cross?

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https://en.wikipedia.org/wiki/Arthur_Samuel

https://en.wikipedia.org/wiki/Deep_Blue_versus_Garry_Kasparov

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https://en.wikipedia.org/wiki/AlphaGo

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Source: https://en.wikipedia.org/wiki/Watson_(computer)

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http://fortune.com/2015/10/16/how-tesla-autopilot-learns/

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https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/

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Example: Face Recognition with AI

● Start with random patterns and rules

● Each learning sample tweaks the rules so its prediction

matches the outcome (backpropagation)

Pixels Edges Object parts Object models

→ → →

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Video Archive Search: Demo

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Video Archive Search

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Video Archive Search

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Demo

http://people.ee.ethz.ch/~maderk/viqae/viqae_prerendered2.html

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Region Segmentation

We can take single images or sequences

of images and classify each point into

various pretrained categories. The

following example shows road, tree,

cars, pedestrians as different colors

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Region Segmentation

These regions can then be analyzed and large deviations or unusual scenes can be

automatically searched for and identified

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Content Tagging

Beyond individual classes,

complex scenes can be

broken down into regions

described by captions.

These regions can then be

easily compared, filtered and

searched through.

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Content Tagging

We can take single images or

sequences of images and

classify each point into various

pretrained categories. The

following example shows road,

tree, cars, pedestrians as

different colors

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Content Tagging

Optimizing for specific

detection problems, e.g.

crosswalks.