4Quant uses machine learning and big data analytics to analyze large amounts of image and video data in order to significantly improve searching, finding anomalies, and tagging efficiency compared to traditional methods. Their technology can process high-resolution data at fast rates using commodity hardware and deep neural networks to extract complex information. 4Quant's applications include healthcare for tumor detection, climate change for lake shrinkage analysis, surveillance for crowd counting, and autonomous vehicles for intersection safety assessment.

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Video and Image Analytics with Machine Learning
4Quant
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

9. 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.