Abstract
In many city centers and institutions, the issue of parking is becoming an increasingly serious one. Increasing numbers of people are flocking to cities, and in order to keep pace with the growing volume of drivers, this project introduces several methods for detecting parking space occupation using machine learning methods. The work presents a review of methods used thus far to solve this problem before looking at results from implementations of this system using a Haar-Cascade algorithm and a convolutional neural network. Our results show that it is possible to use these algorithms for the parking space occupancy detection classification problem with a high-degree of accuracy and success.