Modcam
Modcam
Karl-Anders Johansson Tord Wingren
Johan Lenander
Peter Carlsson
Fredrik Hedlund
Bert Nordberg
Jan Erik Solem Bogdan Tudosoiu
Anders Laurin
Investors Founders
Anonymous statistics Our sensor uses computer vision and powerful onboard processing to determine movements and profiles of visitors, anonymously
What’s the gender & age of my customers?
How long do customers stay? Are customers loyal?
How many customers do I have? How busy is it and when?
Which areas are busy and which are un-utilised?
Know Your Customers
All in one solution The most compact sensor with
built-in analytics for anonymous
people tracking analysis in physical
buildings
People Counting Heat map Demographics
One hardware, endless apps Our open software platform is created to breed new exciting applications over time.
MOD.01
Sensor
MOD.Connect
Device Management Algorithms
Apps Cloud
Sensor Data
Analytics
Web Experience
Complete Stack
INFRASTRUCTURE CONTENT
People Count
Heat Map
Fitting Room
Bench
PIR Above mirror
PIR top down roof
Bench
PIR Above mirror
PIR top down roof
Bench
PIR Above mirror
PIR top down roof
Bench
PIR Above mirror
PIR top down roof
Bench
PIR Above mirror
PIR top down roof
Bench
PIR Above mirror
PIR top down roof 5 min 10 min
5min 25min
5min 2min
15min
0min
How busy is the fitting room?
How big is the queue?
Expected time for the next free room
Bench
PIR Above mirror
Expected free in 25min
Expected free in 5min
Expected free in 15min
Expected free in 25min
Expected free in 28min FREE
In Mall Analytics
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People Counter
People Counter
People Counter
People Counter
People Counter
People Counter
People Counter
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People Counter
People Counter
People Counter
People Counter
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People Counter
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People Counter
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People Counter
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People Counter
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People Counter
Busy stores >75% of mall visitors
>50 % of mall visitors
>30 % of mall visitors
>20 % of mall visitors
<10 % of mall visitors
Queue formation
<5 min <9 min >9 min
Queue too long Alarm
queue build up
Queue busting
>75% probability
>50 % probability
>25 % probability
<25 % of mall visitors
Seat Occupancy
8 9 10 11 12 13 14 15
6
5
4
3
2
1
0
How busy is my conference room?
How busy is my office?
How busy is any place in a building?
WiFi Sensing
Average time spent nearby a location
How many devices – ”occupancy pulse”
Re-occurring devices – loyal customers
Leveraging Smartphone
Demo graphics
Male 28 Confused
Female 22 Neutral w/ hat
Quad-core Device Management
own & 3rd party apps
Designed in Sweden
Bluetooth Low Energy
Micro-USB wall charger
Power over Ethernet
Accelerometer
WiFi
5 x 5 x 2cm, 79gram
Gyro
Features
API for 3rd party clouds Use your own platform for analytics and
insights through a REST API that allow for
easy and flexible retrieval of various types
of data produced by Modcam devices.
Integrated Analytics The Modcam solution comes with a
complete web experience: login and view
live sensor data to get the data insights.
Modcam is Different
ONE-STOP-SHOP Everything you need is included
DOWNLOAD APPS Modcam ”App store” Connected via WiFi or PoE
SERVICE MODEL Monthly payment Return any time
SELF-INSTALLED In less than 10 minutes
FLEXIBLE DATA Modcam Dashboard Web
API for 3rd party integration
Vision Step 1 advance people counter
Tracker 1
Tracker 2
Tracker 3
Count number of tracks generated by objects
Object 1
Object 2
Object 3
Vision Step 2 advance people counter with handover between sensors New Heat Map
212 people
522 people
Track 188 Object ID 188
Track 188 Object ID 188
Generate number of objects in an area and handover of same Object ID between sensors
Vision Step 3 tracker ID + Demographics Tracking without GPS!
212 people 40% male 35-45 age
Gender & Age distribution per area
Demographics
08 19
14:00 – 15:00
Predicted
486 people 62% female 45-55 age
588 people 58% female 35-45 age
Computing Prediction Learning
08 19
14:00 – 15:00
Predicted
The Modcam solution is following it’s
computing path pushing more and more
algorithms while it is using data for
prediction and learning
Modcam AI #Chair
1 0 1 1 0 0 1 0 0 1 1 0
gather analyse tag Index Action
Modcam AI
pic1 pic2
Tracker 1
1st step - prediction
2nd step - object recognition based on tracker movement
3rd step - context categorisation
Tracker 2
Tracker 1
Tracker 2
pic1
pic2
pic3
Thank you