Minimizes boarding time at the gate and ensures on time departures by predicting and preventing overhead bin overflow. We use image recognition to identify the types of luggage (bag, backpack, suitcase, etc.) each passenger possesses. Once the type of luggage is known, an estimation of its dimension is used to calculate the amount of space it will take in the overhead compartments. As the overhead capacity fills up, the gate agent can monitor the capacity status on his/her monitor and make smart decisions about whether or not a bag should be checked in.

Check us out on DevPost! We won 3rd in Delta's Best Air Travel Solution.

• OpenCV
• Flask
• Socket.io
• Microsoft Cognitive Services API

• Flask
• Flask-SocketIO
• IP Webcam on your Android device
• OpenCV for Python

In config.py

• Add these contents and save

deltaKey = 'your delta api key'
CVip = 'the ip of the computer running the CV code'
CVport = the port number of the computer running the CV code as an integer

In daRealThing.py

• Set the camera ip/port - streamURLS
• Set the ip/port of the webapp - webpageIP
• Add the Microsoft Cognitive Services API Key - _key

$ export FLASK_APP=app.py
$ flask run --host=0.0.0.0

The webapp should be served on http://localhost:5000

$ python daRealThing.py

Pressing the enter key with your computer focused on the webapp page will trigger the CV code