This presentation, delivered by Jag Minhas, CEO and Founder, Sensing Feeling, was the first presentation of the Implementing AI: Vision Systems Webinar.

2. sensingfeeling.io
Vision 2020
The journey from research
lab to real-world product
Jag Minhas
CEO & Founder
Sensing Feeling
Agenda
1. Quick about us
2. Key learnings on our journey
3. Case studies from along the way
4. Conclusion

3. sensingfeeling.io
Timeline
Jul 2016
UK Government
R&D funding
Jul 2017
RocketSpace Tech
Ecosystem
Feb 2018
R/GA IoT
Ventures
2016 2017 2018 2019
May 2018
1st product
launched
Oct 2017
Patent filed
Sep 2017
Bench prototype
produced
Feb 2019
Telefónica
2020
Sep 2019
ZAG/BBH
Investors:
Partners:
VGC Partners

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The problems
we’re solving
Customer experience surveillance
● Generates increased customer loyalty
and spending but requires repeated
investment
● CSAT, NPS and feedback are often
inaccurate - and always out of date
● Customer ‘survey fatigue’
Security and risk
surveillance
● Good detection and deterrence of
high risk behaviours, but expensive
● Requires lots human observation to
prevent costly incidents
● Mostly only used ‘after the event’
(recorded evidence)

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Our solution Advanced human behaviour-sensing products
● Powered by computer vision & machine learning
● Strong focus on privacy and ethics

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How it works
and collect data centrally in
real-time
● Always accurate and up to
date
● Out of the box dashboard
with API for IT integration
We sense behaviours
in real-world spaces
● High risk behaviours in
safety critical spaces
● Customer engagement,
attention, emotional
response & demographics
and improve business
performance
● Reduce costs by improving
user wellbeing and safety
● Increase revenues by
enhancing experiences
to improve user experiences
● Manage safety and detect
risks before costly incidents
occur
● Faster and better targeted

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Visual
sensing of human
behaviours
Body
● Postures & gestures
● Demographics
● Emotional response
Movement behaviours
● Dwelling & occupancy
● Motion & flow paths
● Velocities
● Crowding
Interaction with objects and people
● Attention index
● Stress & fatigue index
● Delight & satisfaction index
Sound from speech

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IoT sensing
Cloud component
● Web-based dashboard
● Aggregated behavioural response
● Motion paths & dwelling heatmaps
● Real-time visualisations
● Alerting & triggering
● Real-time Web API
Edge component
● Software implemented on standard
low-power System on Chip &
enclosures for easy installation
● Standard camera, HD, UHD 4K, 8K
(Can interwork with existing CCTV)
● WiFi or 4G/5G connectivity
● 10m - > 100m range options
● Embedded into OEM technologies
e.g. digital screens, signage, kiosks
Telemetry

9. sensingfeeling.io
Client
use cases Collaboration spaces
Measuring the effectiveness of
collaborative spaces & meeting rooms
Live media events
Audience engagement & insights at
media events
Events & conferences
Audience engagement & insights at
business expos
Consumer products
Product testing in consumer homes &
focus groups
Audience insights &
engagement
Road transport
Driver safety detection of fatigue and
sleep deprivation
Rail & aviation
Detecting & predicting high-risk human
behaviours & trespass
Metro & mass transit
Anti-social behaviour detection &
suicide prevention
Oil, marine & gas
Stress & fatigue detection on ships at
sea
Safety, wellbeing & risk
management

10. sensingfeeling.io
Key learnings
on the journey
Solving the computer vision problem
is an important but small (and overstated) part of the overall business challenge
Skills & hiring needs change
CTO emerges from skills that become a priority later than from at the start
Market sector engineering
Product engineering
Systems engineering
Vision
algorithms
DNNs etc.
2016
First hire
2018
Second hire
2019
3rd, 4th & 5th hires
2020
6, 7, 8, 9, 10 ...
CTO

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Key learnings
on the journey
Engineering effort to make it ‘easy to buy, easy to sell’
Purchasing perspective
● simple to understand
● simple and fast to deploy
(can even be self-installed)
● easy and fast to change and
update (the edge processing can
be updated 'over the air')
● simple pricing structure
● simple scaling: just add more
sensors at any time
Selling perspective
● easy to understand, it's a sensor
● doesn't involve much technical pre-
sales support
● software as a service model that
delivers stickier/longer revenues
● very easy to price
● very easy to enable more/repeat
purchases - just sell more sensors

12. sensingfeeling.io
Case studies from
along the way
Some of our implementation challenges
1. ‘Rucksack’ demonstrator
1. Massive scaling for real-world visual analytics
1. Off-grid industrialised vision sensing
a. Outdoors
b. remote locations
c. 24 x 7 x 365 continuous operation
d. no visits, no mains electricity

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Case study 1 Rucksack
demonstrator
Why
● To help in selling the idea to investors and early adopting clients
What it must do
● Show off the complete end-to-end system
● Shouldn’t look anything like a computer
● Be able to carried around in my rucksack, alongside laptop and lunchbox
● Be able to plug into a nearby wall socket for power
● Be able to set it up in 60 seconds and get it working in 30
● Be able to show the real-time dashboard in a browser on my mobile phone

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How
● Small form factor SBC with webcam
● Looks nothing like a computer
● Uses 4G backhaul from mobile phone
Case study 1 Rucksack
demonstrator

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Rucksack demonstrator

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Case study 2 Massive
scaling for real-world
visual analytics
Why
● To solve a very real problem: up to 700 CCTV cameras in a large, complex and
crowded set of locations, with only 9 monitor screens in the control room
What it must do
● Surface high-risk human behaviours across the entire estate automatically to the
control room
● Must not involve the transmission of any images or video out of the locations
● Be scalable
● Be affordable
● Be reliable (always on, and always working 24 x 7 x 365)

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How
● Layered architecture
● Modelling to support Bill of Materials (BoM) selection:
○ Identify the principle scaling factors
○ From the CPU vendors
■ Performance benchmarking data
○ From the System vendors
■ Thermal design power (Watts)
■ Power consumption (Watts)
■ Pricing from system vendors (£)
Case study 2 Massive
scaling for real-world
visual analytics

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Massive scaling for real-world ML-powered visual analytics
Sizing for scalability, performance and n+1 redundancy:,
where:
n = Number of cameras
r = Pixel resolution factor
p = Frame rate factor
Number of VPUs NVPU
NVPU = f(n ,r, p) = Anrp
Number of APUs NAPU
NAPU = f(n, n2) = Bn + Cn2+1
Number of DPUs NDPU
NDPU= f(n) = Dn + 1
Scaling constants A, B, C, D to be determined by modelling using
benchmarking data from CPU and system vendors.
VPU
APU
DPU

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System sizing & scaling model
Platform
DNN accelerated Core i7 290 64
Core i3 668 16
Xeon low core 764 16 0.07KW £820 0.4KW 21 16,044 1 6% £17,220 £1.08 £1.07 3 £2,460 18 £14,760
1.5K
W
7.4K
W
£17,22
0
Core i5 960 16
Core i7T 1196 16
Accelerated Core i5 2316 32
FPGA on Core i5 2346 32
Xeon high core 6125 32
0.10K
W £1,889 0.5KW 3 18,375 9 39% £5,666 £0.35 £0.31 1 £1,889 2 £3,777
0.3K
W
1.5K
W £5,666
Xeon high core 6515 32 0.13KW £2,116 0.5KW 3 19,545 9 54% £6,347 £0.40 £0.32 1 £2,116 2 £4,232
0.4K
W
1.5K
W £6,347
Xeon high core 18511 32 0.21KW £6,829 0.5KW 1 18,511 26 14% £6,829 £0.43 £0.37 1 £6,829 0 £0
0.2K
W
0.5K
W £6,829
Benchmarking data System data Decision support
Throughput(FPS)
System
m
em
ory
(G
b)Therm
alDesign
Pow
er
System
unitcost
PSU
rating
persystem
unit
Q
uantity
required
Clustercapacity
(FPS)
Cam
eras
persystem
unit
Residualexcess
capacity
Totalclustercost
CostperFPS
used
CostperFPS
available
Num
berto
purchase
in
now
Expense
now
Num
berto
purchase
for
production
Expense
ofproduction
ClusterTherm
alDesign
Pow
er
Clusterpow
ersupply
rating
Totalinstallclustercost
Input assumptions
Number of cameras = 26
Frame count required = 200 (from FoV)
Min FPS per frame = 4 (SF lab)
Number of models per frame = 20 (SF lab)
Camera required for development = 3 (SF lab)
Cluster FPS required = 16000
Target system for
BoM

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Implementation design and environmental requirements
Physical layout
VPU
VPU
VPU
APU
DPU
PDU
Shelf
Power & cooling
requirements
+ 2.5KW power supply
+ 0.5KW thermal cooling
+ ~32A max current draw

21. sensingfeeling.io
Case study 3 Off-grid
industrialised vision
sensing
Why
● To support a very specific client use case involving vision sensing in outdoor,
unstaffed, remote locations, with no availability of on-grid power
What it must do
● Be weatherproof and vandal proof
● Be able to work continuously 24 x 7 x 365
● Be able to look after itself if anything goes wrong
● Have industrial certifications, e.g. IP, CE, EMC etc.
● Be affordable
● Be ready to deploy in 3 months

22. sensingfeeling.io
How
● Very low-power system engineering
● Renewable energy harvesting
● Condition monitoring & remote management
● Based on standard off-the shelf industrialised components
○ because there’s no time to do custom embedded system development!
Case study 3 Off-grid
industrialised vision
sensing

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Camera
Peripheral
Peripheral
DNN
accelerator
Relay
Sensor VPU
Comms
Gateway
DC power source
LTE
Antenna
Off-grid industrialised vision sensing

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Component Max current mA
Sensor VPU 1380
DNN accelerator 900
Comms 220
Gateway 580
SMA LTE antenna
UPS battery
Peripheral 2000
Peripheral 400
Camera 1200
6,680
Continuous
operation
● Current draw of between 4.3A in continuous operation
bursting to 6.7A on peripherals being energised
● Power requirement of ~ (4.3*5) = 21W continuous,
bursting to 21+(5.9-3.4)*12 = 51W on peripherals being
energised
Off-grid vision sensing BoM & system power requirements

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Off-grid power system components
Off-grid DC power source
Step-down regulator
Deep-cycle battery
Charge controller
Renewable source e.g. solar
Camera
Peripheral
Peripheral
DNN
accelerator
Relay
Sensor VPU
Comms
Gateway
LTE
Antenna

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Off-grid power system requirements
Step-down regulator
Deep-cycle battery
Charge controller
Renewable source e.g. solar
Assume 85% efficiency, must receive 21/0.85 = ~25W
Which for continuous operation is 25*24 = 600Wh per day
Assume a 12V system, battery must deliver 25/12*24 = 50Ah per day
Assume 70% deep utilisation, a 280Ah battery will provide 12*280*0.7 = 2352Wh
Assume 2 hours solar charge per day in midwinter, must deliver 50/2 = 25A
A 12V panel will need to be rated at 12*25 = 300W
A 300W panel will generate 600Wh per day
Start here
Off-grid DC power source

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Learnings ● Be business-led
Setting out to solve real business problems, with computer vision being one
ingredient in the solution
● A working demonstrator
Which you can carry around in your bag, which just works with no fuss, and
doesn’t look like it was made by a computer scientist
● No free trials
Early-adopting clients undertaking paid trials and PoCs, providing funds for
further product development
● Real-world engineering
Engineering to suit real-world deployment challenges
● Business-centric engineering
Engineering to make it ‘easy to buy, easy to sell’