Real-time Occupancy Detection using Self-learning AI agent

- Team Aficionados

Need for Occupany Detection:

The ability to accurately determine localized building

occupancy in real time enables several compelling

applications, including intelligent control of building systems

to minimize energy use and real-time building visualization

There are several ways to perform occupancy driven

detection using various sensors such as augmented PIRs,

CO2 sensors, etc,.

Drawbacks of current systems

The current methodology of occupancy detection employs

several

Sensors which are not cost – efficient and also involves

numerous proprietary technologies in play.

The systems are also not `smart` enough to identify

occupants inside an environment.

Proposed System

Our Proposed system of using Self-learning AI agent utilizes

camera feeds from CCTV, Web cams to perform accurate

estimation of occupants inside an environment.

By employing `Internet of Things`, the system is smart to

detect occupants using Network activity and also classify the

occupants for real-time updating of occupants information.

Software Stack

CCTV FOOTAGES

WEBCAM FEEDS

Python(Face Recognition

System)

REDIS DATA STORE

Node JS ServerZeroMQ

Node JS ServerClient Dashboard, App

Why Redis, not Mongo?

In-Memory NoSQL Key-Value store, offering soft real-time

updation of data.

Can utilize “pub-sub” service in Redis to subscribe for

changes in data in real-time from the client’s dashboard

Relatively Less overhead read and write, but volatile storage

Internet of Things at Play

Most devices in the present day are equipped with cameras

and networking features that can be utilized to

communicate with each other for estimation of occupants

Traces Network activity from devices also to determine the

occupants information for consideration

Vithara – Smart Dashboard

Vithara – Smart Dashboard

Our system comes with a smart dashboard called “Vithara”

that allows to easily visualize occupants information

Occupants data can also be traced in real-time from maps

and also features a real-time search to target a particular

occupant

Vithara – Smart Camera

The system processes the video feeds in a smarter way by

recognizing the Bar/QR codes from staff IDS and also

classifies the occupants as new visitors using Face

Recognition technique.

Occupants data can also be traced in real-time from maps

and also features a real-time search to target a particular

occupant.

Each camera also features a GPS co-ordinate to identify the

users in the map

Challenges

It is tedious to aggregate the data from various camera

feeds. We use and approach to merge the users’ data from

different feed and also employ machine learning technique

to predict the occupants’ next location.

Outcomes

Better approach for occupancy estimation at minimal cost

Uses available low-cost technologies for determining the

occupant

Easy to scalable and deploy in multiple environments

Drop-in replacement for any occupancy detection system

Thank You!