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Title:Towards accurate person re-identification by deep learning
Author(s):Fu, Yang
Advisor(s):Huang, Thomas S
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Person re-ID
Deep Learning
Abstract:Artificial intelligence surveillance has become increasingly popular due to its security applications. Within this field, person re-identification (re-ID) is a. crucial topic, which aims at matching images of a person in one camera with the images of this person from other cameras. Considering the intensive appearance change of images of the same person, such as lighting, pose and viewpoint, person re-ID is a very challenging problem. In this thesis, we advocate addressing person re-ID by deep learning based methods, which have shown a much better representation ability and much stronger robustness to input data variation and corruption, compared to the traditional approaches. This thesis covers a series of problems involving re-ID including imagebased person re-ID and video-based person re-ID. We start by providing an overview of person re-ID. Following this, we show how to address different reID tasks accurately and efficiently, and our efforts have led to top-performing algorithms on all tasks. The thesis will conclude by describing several promising directions for future research.
Issue Date:2020-04-14
Rights Information:Copyright 2020 Yang Fu
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05

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