CHOMG is our security app that uses motion detection technology, turning webcams that are connected to a Raspberry Pi into advanced security devices. It monitors your space for unexpected movements, notifying you of potential security breaches. Our system processes and sends data to a digital ocean cloud server, allowing users to receive real-time alerts through a sleek, user-friendly app. This ensures customers can monitor their property's safety from anywhere, offering peace of mind with ease!

To use CHOMG, you simple have to connect your raspberry pi (with all motion-detection scripts pre-installed on) to your USB camera. Once this is complete, simply place the camera in an appropriate location create an account on our app, log in and reap the security benefits!

The directories featured in this master branch are all the branches we utilized in CHOMG's development

• imageDetection
• appBackEnd
• appDev
• emailSending
• raspberryPi

For this "branch" to run you can just run CHOMG.py. When this being ran, valid server certificarte, public key algortihm key and environment file with all the users username, password and encryption key.

This "branch" contains all the necessary components to run the Node.js server that hosts the app.

This branch features everything need to run in android studio. This includes a lot of files and dependencies. Gradle is the build model we used and would need to be installed.

Small branch that jut hosts dailyEmail.py script which runs on the Node.js server. The file is hardcoded as it is for development purposes, however commented out at the bottom is the mongoDB implementation that would be used in true deployment.

Script in crontab to run CHOMGs upon setup

For any queries or concerns we are reachable on our email address; [email protected]

Contributors names and student numbers

Amy Marie Craven        121401096
Liam Healy              121447212
Luca Gahan              121352981
Marian Angeles Mendoza  121374793
Jack O Sullivan         121316523

Inspiration, code snippets, etc.

• Haar-based Detectors For Pedestrian Detection" by Hannes Kruppa and Bernt Schiele from ETH Zurich, Switzerland
• README-Template.md by DomPizzie