State-of-the-Arts Person Re-Identification Using Deep Learning

Abstract

Person Re-Identification has become prominent because of various reasons majorly due to its high-performance methods based on deep-learning. It is the process of person recognition from various images captured by different cameras. Provided two set of images the purpose is to find that the given set of images are identical or not. Person Re-Id is often a challenging task due to the similarity in nature like people with identical features, color or clothes. Images are taken from various angles and distances of a given subject in order to achieve high accuracy, so it identifies correctly. Re-Identification has broadly two major categories: i) Image Re-ID and ii) Video Re-ID. Based on the category Re-ID has numerous applications like robotics, automated video surveillance, forensics and multimedia that are deployed using various public datasets like Market1501, VIPeR, MARS, CUHK01, CUHK02, CUHK03, DukeMTMC-reID, MSMT17 etc. In this paper we aim to briefly discuss the process, datasets, recent work on Re-ID, challenges, its approaches and techniques that has been implemented using deep learning systems.