Campus Lost & Found

Campus Lost & Found 🔎🎒
Campus Lost & Found is a project we built as a team during Hacktoberfest Hack Day – Coimbatore x iNTI Club. We wanted to work on a problem that students can actually relate to — losing personal belongings on campus and having no simple way to find them again. Instead of depending on multiple WhatsApp groups, asking friends, or visiting different offices, we wanted to create one centralized platform where students can report, search for, and claim lost and found items.
💡 The Problem
Losing something on a college campus can be surprisingly stressful. It could be an ID card, wallet, calculator, charger, earbuds, water bottle, notebook, or any other personal belonging. At the same time, someone who finds an item may not know who it belongs to.
Usually, students have to:

• Ask around in WhatsApp or class groups
• Post pictures manually
• Ask friends or classmates
• Check with security or the lost-and-found office
• Hope that the person who found the item sees their message The information is often scattered across different places, making the process slow and difficult. 🚀 Our Solution We built Campus Lost & Found as a centralized platform for managing lost and found items on campus. A student can report an item as lost by providing details such as:
• Item name
• Description
• Category
• Location
• Date
• Image Similarly, someone who finds an item can create a found-item report. Other users can then search and filter the available reports to look for potential matches. The platform also includes a claim system, allowing users to submit a claim when they believe they have found their lost item. 🤖 AI-Assisted Image Matching One of the main ideas we explored is using image similarity to help connect lost and found items. For example, if a student loses a pair of wireless earbuds, they can upload an image of them. If another student has reported a visually similar pair as found, the system can help identify it as a potential match. The basic workflow is: Lost Item Image ↓ Image Processing ↓ Similarity Matching ↓ Potential Matches ↓ User Verification ↓ Claim

The purpose of this feature is not to automatically prove ownership, but to reduce manual searching and help users discover possible matches more easily.
🛠️ Technology Stack
Frontend

• Next.js
• React
• TypeScript
• Tailwind CSS Backend
• Python
• FastAPI
• SQLAlchemy
• Pydantic Database
• SQLite for local development
• PostgreSQL/Supabase-compatible architecture AI / Image Processing
• Image similarity techniques
• Computer vision/image processing Development
• Git
• GitHub
• VS Code 🏗️ Backend Architecture We structured the backend into separate components so that the application is easier to maintain and extend. backend/ │ ├── main.py ├── database.py ├── requirements.txt │ ├── models/ │ ├── item.py │ ├── user.py │ └── claim.py │ ├── routes/ │ ├── items.py │ ├── users.py │ └── claims.py │ └── services/ └── item_service.py

The routes handle API requests, the models represent our data, and the services contain reusable application logic.
👥 Our Team
We divided the project into different areas so that each team member could focus on a specific part of the application.
Darshan P — Frontend Development
Worked on the user interface, main pages, user flows, and frontend-backend integration.
Nambi Prasanna B. — Backend Development
Worked on the FastAPI backend, API routes, application logic, backend structure, and frontend-backend integration.
Sachin Kannappan — AI / Image Matching
Worked on the image matching component and explored how visual similarity could be used to identify potential matches.
Abhinav Baru — Database
Worked on the database structure, models, and relationships between users, items, and claims.
🔥 Challenges We Faced
One of our main challenges was integrating the different parts of the application. The frontend, backend, database, and AI components all had to communicate correctly.
Another challenge was designing the database structure around users, items, and claims while keeping the system flexible for future features.
Image matching was also challenging because two images of the same type of object can look very different depending on lighting, angle, background, and image quality.
Since this was a hackathon, we also had to decide which features were realistic to complete within the available time and which ones could be added later.
📚 What We Learned
Working on this project gave us practical experience with:

• Full-stack application development
• REST APIs
• FastAPI
• Database design
• Frontend-backend integration
• Image processing
• Git and GitHub
• Team collaboration
• Debugging and testing
• Building under hackathon time constraints More importantly, we learned how to take a real-world problem, break it down into smaller technical problems, and work together to turn an idea into a working prototype. 🔮 Future Improvements There are several things we would like to explore in future versions:
• More accurate image matching
• Advanced image embedding models
• Real-time notifications
• Campus-specific authentication
• Location-based matching
• QR codes for found items
• Improved claim verification
• Admin dashboard
• Mobile application
• Cloud deployment
• Better spam and fraud detection 🎯 Final Thoughts Campus Lost & Found started with a simple question: Can we make it easier for students to find their lost belongings?

Our goal was to combine a centralized lost-and-found platform with AI-assisted image matching to make the process faster and more convenient.
The project is still a starting point, and there is a lot we can improve, but building it during a hackathon has been a great learning experience for our entire team. We hope to continue developing it and eventually explore how it could be used on a real college campus.
🔗 Project Links
GitHub Repository:
https://github.com/Nambi25/campus-lost-and-found
Live Application:
nimble-dusk-3e3fae.netlify.app
Demo Video:
https://youtu.be/UttsYPIDUpw
🙌 Acknowledgements
A big thanks to the organizers of Hacktoberfest Hack Day – Coimbatore x iNTI Club for giving us the opportunity to build, experiment, collaborate, and learn together.