## Inspiration

We as a team shared the same interest in knowing more about Machine Learning and its applications. upon looking at the challenges available, we were immediately drawn to the innovation factory and their challenges, and thought of potential projects revolving around that category. We started brainstorming, and went through over a dozen design ideas as to how to implement a solution related to smart cities. By looking at the different information received from the camera data, we landed on the idea of requiring the raw footage itself and using it to look for what we would call a distress signal, in case anyone felt unsafe in their current area.

## What it does

We have set up a signal that if done in front of the camera, a machine learning algorithm would be able to detect the signal and notify authorities that maybe they should check out this location, for the possibility of catching a potentially suspicious suspect or even being present to keep civilians safe.

## How we built it

First, we collected data off the innovation factory API, and inspected the code carefully to get to know what each part does. After putting pieces together, we were able to extract a video footage of the nearest camera to us. A member of our team ventured off in search of the camera itself to collect different kinds of poses to later be used in training our machine learning module. Eventually, due to compiling issues, we had to scrap the training algorithm we made and went for a similarly pre-trained algorithm to accomplish the basics of our project.

## Challenges we ran into

Using the Innovation Factory API, the fact that the cameras are located very far away, the machine learning algorithms unfortunately being an older version and would not compile with our code, and finally the frame rate on the playback of the footage when running the algorithm through it.

## Accomplishments that we are proud of

Ari: Being able to go above and beyond what I learned in school to create a cool project

Donya: Getting to know the basics of how machine learning works

Alok: How to deal with unexpected challenges and look at it as a positive change

Sudhanshu: The interesting scenario of posing in front of a camera while being directed by people recording me from a mile away.

## What I learned

Machine learning basics, Postman, working on different ways to maximize playback time on the footage, and many more major and/or minor things we were able to accomplish this hackathon all with either none or incomplete information.

## What's next for Smart City SOS

hopefully working with innovation factory to grow our project as well as inspiring individuals with similar passion or desire to create a change.
## Inspiration

Want to take advantage of the AR and object detection technologies to help people to gain safer walking experiences and communicate distance information to help people with visual loss navigate.

## What it does

Augment the world with the beeping sounds that change depending on your proximity towards obstacles and identifying surrounding objects and convert to speech to alert the user.

## How we built it

ARKit; RealityKit uses Lidar sensor to detect the distance; AVFoundation, text to speech technology; CoreML with YoloV3 real time object detection machine learning model; SwiftUI

## Challenges we ran into

Computational efficiency. Going through all pixels in the LiDAR sensor in real time wasn’t feasible. We had to optimize by cropping sensor data to the center of the screen

## Accomplishments that we're proud of

It works as intended.

## What we learned

We learned how to combine AR, AI, LiDar, ARKit and SwiftUI to make an iOS app in 15 hours.

## What's next for SeerAR

Expand to Apple watch and Android devices;
Improve the accuracy of object detection and recognition;
Connect with Firebase and Google cloud APIs;
## Inspiration

This weekend at Deltahacks we were challenged to hack for change and the betterment of our communities. The Innovation Factory Hamilton challenged the competitors to look to the future and help build a more connected city that is better enabled to serve it's inhabitants. We used this as inspiration for building our application. It has the potential to save lives by cutting down on response time for dangerous situations such as a car crash or an active threat.
This was our chance to "imagineer" a part of the future

## What it does

Smart Response is made to minimize the overhead time notifying first responders when there has been an incident that is potentially life-threatening. Using the cityIQ API we connected to the Hamilton cityIQ nodes to receive real-time audio and video from nodes around the city. Using the audio data, we trained and implemented a model using Python to detect when there has been a significant event and then notify first responders or the public of a threat. The application would then geotag the location of the incident with a real-time image and classify it (car crash, gunshot etc.) based on the audio properties. First responders can view the image to determine the severity of the incident and determine what actions to take next.

## How we built it

We built this system using many technologies.

Front-end app:
React Native and Apple Maps

Back-end:
Node.js + express, Python, Math and machine learning libraries such as librosa, openCV, matplolib, scikit, tensorFlow; and finally PostgreSQL for the database

API:
We used the cityIQ api to get live audio and images of Hamilton.

## Challenges we ran into

We ran into many challenges over the course of the project. Connecting to the cityIQ API was a challenge we faced at the beginning of the project, but we persevered and were able to receive data from the node. Later building and training a model for classifying mel spectrogram images was a challenge as it was our first time using Machine Learning and the Python libraries associated with it. Also building digital filters to help remove most of the ambient noise present in the audio data was a challenge as it was more of a trial and error to receive that data and determine a filter that worked best for filtering noise but still being able to classify different sounds.

## Accomplishments that we're proud of

Every group member contributed and learned lots for such a short period of time. It was challenging to stay focused when facing many issues along the way, but we are all proud for staying the course and building the platform. We are proud of the fact that what we built has the potential to impact people on a day to day basis.

## What we learned

Our group stepped out of our comfort zone and all learned something new. It was our first time developing an iOS app in react-native as well as use iOS Map API. We also learned the ML and signal processing libraries in Python.

## What's next for Smart Response

Expand the number of connected nodes for serving the community. Also use the nodes to obtain more training data for the model as well as training it with other common urban noise data to accurately analyze a larger range of disturbances.