  Person Re-Identification (Re-ID) is an important problem in computer
vision-based surveillance applications, in which one aims to identify a person
across different surveillance photographs taken from different cameras having
varying orientations and field of views. Due to the increasing demand for
intelligent video surveillance, Re-ID has gained significant interest in the
computer vision community. In this work, we experiment on some existing Re-ID
methods that obtain state of the art performance in some open benchmarks. We
qualitatively and quantitaively analyse their performance on a provided
dataset, and then propose methods to improve the results. This work was the
report submitted for COL780 final project at IIT Delhi.
