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Person Re-identification: State of the Art and Future Trends
Liang Zheng, Yang Yang, and Shengcai Liao
Monday, August 20, 2018, 14:00-17:00, 307B, 3rd Floor
Tutorial Abstract
The task of person re-identification aims to find a queried person in a large database of pedestrian images, so that the person-of-interest can be located across cameras. This task underpins critical research and application significance, and in recent years has received fast increasing attention from both the academia and industry. Traditionally, person re-identification is featured by effective combinations of visual descriptors and similarity metrics. At present, the research frontier has been advanced to the deeply learned invariant feature embeddings which are both discriminative and efficiency friendly. Moreover, many research tasks have been introduced to the community, such as video-based, language-based, and detection-informed re-identification. The rich scientific possibilities thus have given rise to a prime of person re-identification research.
In this context, this tutorial targets at bringing together the current research advances and discussing the state of the art and future trends in person re-identification. The tutorial will review the traditional research initiatives in this area, present an overview of the current frontier, and finally discuss the possible future research directions. Through this tutorial, audience will not only have a more comprehensive knowledge of person re-identification, but also gain a research vision that may expand their own research capacities.
In this context, this tutorial targets at bringing together the current research advances and discussing the state of the art and future trends in person re-identification. The tutorial will review the traditional research initiatives in this area, present an overview of the current frontier, and finally discuss the possible future research directions. Through this tutorial, audience will not only have a more comprehensive knowledge of person re-identification, but also gain a research vision that may expand their own research capacities.
Tutorial Outline
Ø A general introduction and overview of person re-identification (40 min)
Ø The seamless corporation of visual descriptors and similarity metrics (1h)
Ø Deep learning architectures and future research possibilities (1h 20min)
Ø The seamless corporation of visual descriptors and similarity metrics (1h)
Ø Deep learning architectures and future research possibilities (1h 20min)
Speaker Introduction
Dr. Liang Zheng (liangzheng06@gmail.com) is a postdoc researcher in the University of Technology Sydney. Prior to joining UTS, he obtained his B.E and PhD degrees from Tsinghua University. His research interest is large-scale person re-identification, image retrieval, and deep learning. He has published 21 papers in TPAMI, IJCV, CVPR, ICCV, ECCV, and IEEE/ACM Transactions. He makes recognized attempts in large-scale person re-identification by conceptually uniting the fields of person re-identification and image retrieval. He has introduced five large-scale datasets/evaluation protocols which have become the de facto benchmarks in this field. His works are extensively cited by the community and the most highly cited paper receives 200+ citations within two years of publication. Dr Zheng received the Outstanding PhD Thesis from Chinese Association of Artificial Intelligence and the Early Career R&D Award from D2D CRC, Australia. His research was featured by the MIT Technical Review and selected into the computer science courses in renowned universities such as Stanford University and the University of Texas at Austin. Web page: http://www.liangzheng.com.cn/index.html
Dr. Yang Yang (yang.yang@nlpr.ia.ac.cn) received the B.S. degree and M.S. degree from Xidian University, in 2009 and 2013, respectively, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, in 2016. He is currently an assistant professor in National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. His research interests are in pattern recognition, computer vision, image processing, and machine learning, and particularly in person re-identification, attribute analysis, and face recognition. He has published 12 papers in AAAI, ECCV, ICPR, ICIP, IJCNN, ICB and PR. His works are extensively cited by the community and the most highly cited paper receives 200+ citations. Web page: http://www.cbsr.ia.ac.cn/users/yyang/main.htm
Shengcai Liao is an Associate Professor in the Institute of Automation, Chinese Academy of Sciences (CASIA). He is a Senior Member of IEEE, and a Member of the Technical Committee on Computer Vision, CCF. He received the B.S. degree in mathematics and applied mathematics from the Sun Yatsen University in 2005 and the Ph.D. degree from CASIA in 2010. He was a Post Doctoral Fellow in the Department of Computer Science and Engineering, Michigan State University during 2010-2012. His research interests include computer vision and pattern recognition, with a focus on image and video analysis, particularly face recognition, object detection, person re-identification, and video surveillance. He has published over 70 papers, with over 5000 citations according to Google Scholar. He was awarded the Best Student Paper award in ICB 2006, ICB 2015, and CCBR 2016, and the Best Paper award in ICB 2007. He was also awarded the best reviewer award in IJCB 2014. He served as an Area Chair for ICPR 2016, ICB 2016, and ICB 2018, and as a PC member for ICCV, CVPR, ECCV, etc. Web page: http://www.cbsr.ia.ac.cn/users/scliao/