Towards accurate person re-identification by deep learning
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.
University of Illinois - Urbana-Champaign
Networked Digital Library of Theses and Dissertations
University of Illinois at Urbana-Champaign