The aim of this project was to train different machine learning models to detect red barrels within new test images collected by a mobile robot and provide their location coordinates.
For this, I implemented color segmentation by training up to 12 models corresponding to different pixel colors. I used Multivariate Gaussian Probability Density Functions (PDFs) to represent each color distribution, and Bayes Theorem to calculate the probability of a new pixel belonging to each of the classes. I explored different color spaces and techniques to identify connected components and localize the barrel coordinates.
Check out the full project report by clicking below!
