Developing location tracking and deep learning based solutions to help the homeless
Homelessness is a major problem in the nation. A report published by the White House addressed this with 552,830 people being counted as homeless in the year 2019 in the entire country. There are thousands of NGOs & Shelter homes working for the upliftment of these people but the results are not satisfactory. False reports of homeless, inefficient tracking of the rough sleepers and no co-operation between various NGOs drove us to use technology oriented solutions to function for the greater social good.
What it does
The Shelter Helper Mobile site greets you with a home page consisting of prompts for either reporting a homeless or asking for help. If you report a homeless person, you can just click a picture and give location access. Shelter Helper takes the image and sends it along with the label which tells the probability of the picture being of an actual person in distress. The coordinates of the place where the picture was clicked are sent to a nearby NGO who can then take further required steps.
How we built it
Challenges we ran into
Accomplishments that we’re proud of
Ultimately, the best thing over the course of the Pyghack 2020, was us collaborating over a period of 48 hours, trusting each other, improvising on our mistakes and facing challenges together. Completely compiling a fully functioning mobile site with sophisticated backend and model integration in such a short span of time is something we’ll cherish whenever we’ll think of the time when we participated in a hackathon, remotely, in the middle of the pandemic. What we learned : In the social aspect, sincere analysis of the problem in frame and realising the seriousness of the issue helped us understand one of the many social problems people face in day to day life which everyone cannot easily relate to. On the technical counterpart, we had an idea how to develop our parts but bringing the whole site to life and integrating all the minor aspects to develop a beautiful (both aesthetically and socially) end product helped us learn a lot.
What’s next for us
We can expand by first connecting with neighbouring shelter homes. By having an exhaustive database of such NGOs we will be able to give better results. As the backend involves running a deep learning model, it takes a lot of time for computation locally. We can use services like AWS or Azure to upload our model as well as the database online for easy and expandable usage. This will save on computational time, making the backend more lightweight. If the idea gains traction, we can collaborate with surrounding states and counties to contribute towards the social cause.
The Team -
Shashvat , Sajal , Karan and Aryansh