In today’s fast-paced world, pets occasionally go missing, causing distress for both owners and the animals themselves. The traditional methods of reuniting lost pets with their owners involves posting flyers, contacting local shelters, or posting on social media. All of these methods aren’t effective and are very time-consuming. Furthermore, there is a lack of real-time, community-driven solutions to aid in the recovery of lost animals.
As aforementioned, there is a lack of real-time, community-driven solutions to aid in the recovery of lost animals. We propose an end-to-end solution that leverages modern technology to revolutionize the process of reporting found animals, their identification, and subsequent reunification with their owners. We propose a web application that matches up pet owners with their lost pets if they go missing. The status quo at the moment sucks; traditional methods of reuniting lost pets with their owners involves posting flyers, contacting local shelters, or posting on social media. None of these are nearly as effective, the pipeline of getting a lost animal to its owner is extremely disjoint and there isn’t a clear flow defined. We built Tail TrackR — an application that allows users to upload pictures of lost animals + where they were found into our app. From that point onwards, we have a ML back-end which classifies the animal and any significant features i.e the breed, colour, eye colour, etc. Once this is done, the nearest animal shelter is automatically contacted and this information is fed into their database. For the owner, they can simply login to the app and they can say they lost an animal that has certain features and if a similar animal enters our database, they will be notified.
Languages: Javascript, Python
Frameworks: React, Flask, Tensorflow, PyTorch
Database: MongoDB
Dev Shah: ML researcher @ UofT, Director @ UofT AI, Assistant VP of Engineering @ UTMIST, Prev. ML Engineer @ Interactions, ML Developer @ PhotoML, Research Intern @ Interac
Inaam Azeezur-Rahman: Software Engineer, experienced in Python, Javascript and related web frameworks. Completed internships in the past regarding web-development and cybersecurity.
Muhammad Hamza: Software Engineer and MCSS TA, expert in python, JavaScript, C/C++ and ML. Worked on projects related to computer vision.
Dev Vora: Software developer: Full stack developer, CS TA, back-end specialist, proficient in Python (web frameworks & data technologies) and Javascript (MERN)
Month 1: Functional dog breed model + basics of the UI/UX
Month 2: Implement user dashboard + cat breed model + allow users to create posts
Month 3: Pipeline ML models with animal classification + connect ML model to frontend + user authentication (email verification) + use Google Maps API for location
All of the design choices made for Tail-TrackR can be found in the documentation here.
Before deploying, ensure that you have node and npm installed on your machine. For reference, check here. Also install nvm, for reference, here is how to install.
git clone https://github.com/utmgdsc/TAIL-Trackr.git
cd into ML MODEL and do the following:
pip install -r requirements.txt
Then enter "cd ../flask-server" into the terminal (note: do not enter the quotes).
pip install -r requirements.txt
python3 server.py
Then enter "cd ../client" into the terminal.
npm i
npm start
