title 44pt title case the computer vision revolution ... · the computer vision revolution:...

Title 44pt Title Case

Affiliations 24pt sentence case

20pt sentence case

© ARM 2017

The Computer Vision revolution: Teaching machines to see

Steve Steele

ARM Tech Forum Taipei

Director, Product Marketing

Imaging & Vision Group, ARM

July 4th 2017

© ARM 20174


Bullets 24pt sentence case

bullets 20pt sentence case

Agenda

• ARM and the origins of IVG

Introduction

• The growing importance of cameras

• The challenge of computer vision

It's a visual world

• The more obvious

• The less obvious

Computer vision use cases

Problem and solution

Future directions

Ecosystem opportunities

© ARM 20175




Who am I?

▪ Steve Steele, Director of Product Marketing - Imaging & Vision Group

▪ 16 years at ARM in various marketing roles

▪ Physicist, turned electronics engineer…

▪ … turned software engineer…

▪ … turned marketing dude

▪ Photographer, fiddle player, etc

© ARM 20176




The ARM visionTechnology that invisibly enables opportunity for a globally connected population

© ARM 20177




The ARM missionDeploy energy-efficient, ARM-based technology wherever computing happens…

© ARM 20178




Apical/IVG - pioneers in imaging and vision

© ARM 20179




Computer Vision will be revolutionary

It’s a visual world

© ARM 201710




Many sensors today are proxies for vision

▪ Passive Infrared Sensors – movement rather

than accurate people detection

▪ IR beam – for non-specific people counting

▪ Magnetic sensors – detect open doors or

windows

▪ Distance sensors – detect cars in spaces and

provide infrastructure data

▪ Many current sensor types can be replaced by

vision-enabled cameras

© ARM 201711



bullets 20pt sentence case Sensors today

will be

replaced by cameras

Computer Vision

will affect

many diverse markets

© ARM 201712




▪ Vision is the most amazing feat of natural intelligence

▪ Living creatures have been seeing for millions of years

Seeing is easy…

© ARM 201713




▪ We humans can extract information from pictures quite easily

▪ It’s not so easy for computers….

…Teaching computers to see is harder

By U.S. Department of Agriculture (20120105-OC-AMW-0420)

[CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons

© ARM 201714




Computer vision feeds into machine learning

Robotics Home security

▪ Machine learning makes smart connections to previously encountered concepts

▪ It’s useful when:

• We don’t have algorithms…

• but we do have a lot of data

Image recognition

© ARM 201715




▪ Team at UC Davis recently:

▪ Trained (supervised learning)

▪ Pigeons (organic convolutional neural networks)

▪ To recognize benign and malign breast cancer

(binary classifier)

▪ Ensemble of 4 pigeons at 99% accuracy

Pretty hot field now – even without machines

https://www.ucdmc.ucdavis.edu/welcome/features/2015-2016/11/20151118_pigeons.html

© ARM 201716




16

© ARM 201717




Computer vision real use cases

AR & VRAutomotive

© ARM 201718




Computer vision real use cases

AR & VR Computational

photography

Video analyticsAutomotive

© ARM 201719




Computer vision case study: autonomous drones

Odroid XU4- Cortex-A15/A7

- Mali-T628

Stereoscopic

image pair

Depth map

© ARM 201720




Some use cases are obvious… but others less so

© ARM 201721




Vision technology to prevent heart failure

▪ Swollen ankles provide early warning of

heart failure for high risk patients

▪ HeartFelt Technologies in-home cameras:

▪ Detect the ankle

▪ Model in 3D

▪ Monitor for change

▪ Seamlessly notify doctors if

required

© ARM 201722




Camera unit installed FoC

Detection made, doctor

notified

Medicine prescribed,

hospital visit avoided

Hospitalpays

Vision technology to prevent heart failure

© ARM 201723




Streamlining waste haulage:

▪ Smart cameras added to skips see when the

skip is nearly full

▪ Automatically advise base

▪ Waste pick-ups scheduled only when required

▪ Reduces pick-ups by 50%

Business model:

▪ Initial implementation revenue

▪ Recurring revenue

▪ Customers pay per container,

per month

Vision technology in logistics

© ARM 201724




Vision technology in agriculture

CV being used to selectively harvest

crops:

▪ Vision allows crops to be picked only

when they’re ready

▪ Autonomous harvesting vehicles further

reduce human interaction

Business model:

▪ Potential for significantly improved yields

for non-uniform crops

▪ Reduction in man power & human error

© ARM 201725




SecurityPresence

detection

Remote

monitoring

Detecting

unusual events

Control of

environment

Vision technology in the home

© ARM 201726




Make full screen

© ARM 201727




Massmarket

economics

Increased processing

power

Improving

CV and ML

techniques

Opportunities for new and

existingmarkets

© ARM 201728




Up to 40MPixsmartphone cameras

4bn cameras

sold per year

>1bn Connected

cameras installed by 2020*

*Gartner forecast, IoT Endpoints and associated services 2016

The time for imaging and computer vision is now…

© ARM 201729




▪ For good human-interaction▪ Low latency is very important

▪ Cloud-response times are likely to be too long

▪ High frame rates are critical for this

▪ Now capable of 30-60fps

▪ Equivalent of gaming response time

▪ Captures not only change but defining event

▪ Fall / accident / security breach

Real-time processing is key

© ARM 2017 30

Text 54pt sentence case

‘A picture is worth a

thousand words...

and uses up a thousand

times the memory.’

Stephen Hawking

Stephen Hawking outside Gonville & Caius College, Cambridge, in

2015 (Lwp Kommunikáció [CC / Flickr])

© ARM 201731



bullets 20pt sentence case ~7.2 exabytes mobile

data monthly*

60%of mobile data is

video*

1 Bn hours watched

on YouTube

every day**

*Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015–2020 White Paper

**https://www.wsj.com/articles/youtube-tops-1-billion-hours-of-video-a-day-on-pace-to-eclipse-tv-1488220851

The data explosion

© ARM 201732




Privacy is critical

▪ Personal details are not required

▪ Only relevant data captured and analyzed at

the edge

▪ Reduced potential for data breach or remote

monitoring

▪ Only select data leaves the

edge device

© ARM 201734




The shape of future solutions

▪ Vision will be everywhere and will change

everything

▪ Sensors will be replaced by cameras

▪ Real-time edge processing and security are

key

▪ Interesting new business models based on

early detection and optimizing processes

▪ We need to optimize the complete system

© ARM 201735





How can ARM help you with computer vision?Computer vision algorithms on ARM CV processors, CPU and GPU

ConvolutionColor space

conversion

Affine transforms

Feature detectors

(variable)

Block matchingOptical flow

Support vector

machines

Semi-global

block matchingFeature matching

Neural networks

Integrated suite of ARM IP

© ARM 201736




Future technology directions

… and more …

CV-

enabled

video

AFBC

Mali

HDR

display

ISP-GPU

bridge Cache

coherency

ISP-video

path

The trademarks featured in this presentation are registered and/or unregistered trademarks of ARM Limited

(or its subsidiaries) in the EU and/or elsewhere. All rights reserved. All other marks featured may be

trademarks of their respective owners.

Copyright © 2017ARM Limited

© ARM 2017

Thank you

謝謝 !

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