Towards accurate person re-identification by deep learning
Fu, Yang
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https://hdl.handle.net/2142/108109
Description
Title
Towards accurate person re-identification by deep learning
Author(s)
Fu, Yang
Issue Date
2020-04-14
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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