Frustrated with current parking procedures, we decided parking lots could be smarter. So we originally wanted to make an app and/or webapp to color where cars are and aren't to help a driver find a spot. However, as we were working, we realized that humans can see a picture just fine and that this technology would work much better for autonomous vehicles that cannot easily recognize objects.
So, we used OpenCV to write a python script that takes an image of a parking lot and tells which spots are empty and which are not. This gives valuable information to all parties.
The autonomous vehicle gets:
- Current data on the closest parking spot (which means it will not be driving around until it finds one)
- To the spot quicker (saving fuel [whether electric, gas, etc.])
The business owner gets:
- Data regarding how full a lot section is
- Customers spending less time in the lot and more time in the store
The best part:
- It can potentially use existing CCTV/security cameras already installed in the lot for the data feed
- Cost is very little considering this can run on a RaspberryPi0
The future:
- Once autonomous vehicles are more popular, this will encourage parking lots to be smarter. This means a car may be able to get data from multiple lots and pick the best one to park in based on spot availability and factor in parking lot prices.
- Less movement in a parking lot means less chances for accidents (Hack-cidents?)
- Humans getting where they need to be quicker
- Clone repository using
https://github.com/umer936/spotAspot.git - Install python and opencv
- Run
python CarsByRows.py
- Fork it!
- Create your feature branch:
git checkout -b my-new-feature - Commit your changes:
git commit -am 'Add some feature' - Push to the branch:
git push origin my-new-feature - Submit a pull request :D
Umer Salman Jorge Rivero Valeria Jara
MIT License