modcam retail facility management mod.01 sensor

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

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

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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

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Tracker 1

1st step - prediction

2nd step - object recognition based on tracker movement

3rd step - context categorisation

Tracker 2

Tracker 1

Tracker 2

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pic2

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Thank you