This repository contains the final results for my second Pattern Recognition Coursework that I completed with my partner Karoly. The report describes person re-ID experiments on the popular CUHK03 dataset that contains pictures of pedestrians taken from two different surveillance cameras. A set of features was already provided. The goal is to find a suitable distance metric which learns a feature transformation yielding improved performance on a range of metrics for kNN-retrieval. The original covariance based Mahalanobis method, Large Margin Nearest Neighbour Distance Metric, Metric Learning for Kernel Regression and a fully connected Neural Network with Triplet Loss are considered for this purpose. The Kernel Regression gave the best results with a performance similar to the untransformed baseline approach.
Input features and data may be provided upon request.