Overview
- Presents context aware, scale pyramid, and multi-scale superpixels to optimize correlation filter based trackers
- Designs memory term, content perception and channel attention for correlation filter with deep feature based trackers
- Proposes attention shake, frequency-aware, and epsilon-greedy to improve deep learning based trackers
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Table of contents (6 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
Weibin Liu received the Ph.D. degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China, in 2001. During 2001-2005, he was a researcher in Information Technology Division at Fujitsu Research and Development Center Co., LTD. Since 2005, he has been with the Institute of Information Science at Beijing Jiaotong University, where currently he is a professor in Digital Media Research Group. He was also a visiting researcher in Center for Human Modeling and Simulation at University of Pennsylvania, PA, USA during 2009-2010. His research interests include computer vision, computer graphics, image processing, virtual human and virtual environment, and pattern recognition.
Jun Wang received the M.S. degree in Pattern Recognition and Intelligent Systems from Hebei University, China, in 2015. He received the Ph.D degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China. He was also a visiting researcher in Visual Object Tracking at University of Central Florida, USA during 2018-2019. Currently, he is an associate professor at College of Electronic Information Engineering, Hebei University. His research interests include image processing, computer vision, visual object tracking and pattern recognition.
Shunli Zhang received the B.S. and M.S. degrees in electronics and information engineering from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the Ph.D. degree in signal and information processing from Tsinghua University in 2016. He was a visiting scholar in Carnegie Mellon University, Pittsburgh, from 2018 to 2019. He is currently an associate professor in School of Software Engineering, BeijingJiaotong University. His research interests include pattern recognition, computer vision, and image processing.
Lihui Wang received the Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, Beijing, China, in 2011. She is currently a lecturer of the Department of Information and Communication, Army Academy of Armored Forces Academy. Her main research interests include computer application, big data analysis, and three-dimensional reconstruction.
Yuxiang Yang received theB.S. degree in computer science and technology from the Northeastern University of China, Liaoning, China, in 2014. He is currently a Ph.D. Candidate at School of Software Engineering, Beijing Jiaotong University. His research interests include deep learning, reinforcement learning, and object tracking.
Bowen Song received the B.S. degree in computer science and technology from the School of Computer Science and Technology, Heilongjiang University, China, in 2018. She is currently pursuing the master’s degree at School of Software Engineering, Beijing Jiaotong University. Her research interests include visual object tracking, and deep learning.
Bibliographic Information
Book Title: Visual Object Tracking from Correlation Filter to Deep Learning
Authors: Weiwei Xing, Weibin Liu, Jun Wang, Shunli Zhang, Lihui Wang, Yuxiang Yang, Bowen Song
DOI: https://doi.org/10.1007/978-981-16-6242-3
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Hardcover ISBN: 978-981-16-6241-6Published: 19 November 2021
Softcover ISBN: 978-981-16-6244-7Published: 20 November 2022
eBook ISBN: 978-981-16-6242-3Published: 18 November 2021
Edition Number: 1
Number of Pages: XIV, 193
Number of Illustrations: 41 b/w illustrations, 84 illustrations in colour
Topics: Image Processing and Computer Vision, Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence