Monitor Parking Spaces - Machine Learning
Budget: $250 – $750 USD
Create a python solution with opencv that can count and classify available spaces, in addition to identifying irregular parking, that is, vehicles parked outside of designated spaces.
It must be possible to run on a Core i5 computer with 8GB or less.
Example videos will be provided if you do not have a trained model ready.
Vacancies are painted like the example images and must be classified into:
Free vacancies (img1)
Occupied Vacancies (img2)
Illegal parking (img3)
The vacancies can be oblique and parallel, as shown in example videos made by drone.
They can be executed with live video or a video file generated by a drone or camera installed at a height where a large number of vacancies can be identified.
The generated code will be part of another solution, so there is no need to worry about the graphical interface, just clean and easy-to-understand code.
At the end, the model must be made available, how it was trained and examples.
It must be possible to run on a Core i5 computer with 8GB or less.
Example videos will be provided if you do not have a trained model ready.
Vacancies are painted like the example images and must be classified into:
Free vacancies (img1)
Occupied Vacancies (img2)
Illegal parking (img3)
The vacancies can be oblique and parallel, as shown in example videos made by drone.
They can be executed with live video or a video file generated by a drone or camera installed at a height where a large number of vacancies can be identified.
The generated code will be part of another solution, so there is no need to worry about the graphical interface, just clean and easy-to-understand code.
At the end, the model must be made available, how it was trained and examples.