Muni-image-recognition | Muni 48 & Noe

App: https://muni-web-app-796082523807.us-west1.run.app/ (off line at the moment)
Project Video: https://www.youtube.com/watch?v=qmK1V2FHKFU

The goal of this project is to track real-time data for San Francisco Municipal (Muni) buses using a local webcam and image recognition to forecast arrival intervals.

Project Overview

• Webapp to forecast public transportation arrival time and expose frequency trends based on image recognition model.

Data Pipeline & Key Features

The project employs the following pipeline to achieve its goal:

• Video Capture: A local webcam captures live video of a bus stop.
• Image Recognition: A YOLO (You Only Look Once) model is used to detect when a new Muni bus arrives at the stop.
• Data Storage: Detection results are parsed and streamed into a SQLite database.
• Forecasting: The system provides forecasts based on the trained data.
• Web Interface: Data is made accessible via a Flask application hosted on Google Cloud Platform (GCP).

Languages, Libraries and Hosting

Based on the repository structure and documentation, the following technologies are utilized:

• Python
• YOLO (Image recognition model)
• Flask (Web framework)
• Docker (Containerization)
• HTML (Front-end interface)
• Google Cloud Platform (Hosting and Cloud SQL)