Overview
- Provides a thorough introduction to the most common problem settings, including semi-supervised VOS and unsupervised VOS
- Discusses recent progress in video object segmentation, including new datasets, methods, and experimental findings
- Aids readers to gain a better understanding of the most important problems and advances via real-world examples
Part of the book series: Synthesis Lectures on Computer Vision (SLCV)
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About this book
This book provides a thorough overview of recent progress in video object segmentation, providing researchers and industrial practitioners with thorough information on the most important problems and developed technologies in the area. Video segmentation is a fundamental topic for video understanding in computer vision. Segmenting unique objects in a given video is useful for a variety of applications, including video conference, video editing, surveillance, and autonomous driving. Given the revolution of deep learning in computer vision problems, numerous new tasks, datasets, and methods have been recently proposed in the domain of segmentation. The book includes these recent results and findings in large-scale video object segmentation as well as benchmarks in large-scale human-centric video analysis in complex events. The authors provide readers with a comprehensive understanding of the challenges involved in video object segmentation, as well as the most effective methods for resolving them.
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Keywords
Table of contents (3 chapters)
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Front Matter
Authors and Affiliations
About the authors
Ning Xu, Ph.D., is a Research Scientist at Adobe Research. He received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He was an organizer of the first and second LSVOS Challenge in ECCV 2018 and ICCV 2019. He is also an organizer for the ACM MM 2020 grand challenge “Large-scale Human-centric Video Analysis in Complex Events” and ACCV 2020 tutorial “Spatial –Temporal Parsing of Objects: From Segmentation to Actions”. His research interests include image and video segmentation.
Weiyao Lin, Ph.D. is a Professor at Shanghai Jiao Tong University. He received his B.S. and M.E. from Shanghai Jiao Tong University and Ph.D. degree from the University of Washington, all in electrical engineering.
Xiankai Lu, Ph.D., is a Research Professor at Shandong University. Prior to this role, he was a research associate with Inception Institute of Artificial Intelligence at Abu Dhabi, UAE. Dr. Lu received a B. E. from the Department of Automation at Shan Dong University.
Yunchao Wei, Ph.D, is a Professor in the Center of Digital Media Information Processing at Beijing Jiaotong University. He received his Ph.D. from Beijing Jiaotong University. His current research interests include visual recognition with imperfect data, image/video segmentation and object detection, and multi-modal perception.
Weiyao Lin, Ph.D. is a Professor at Shanghai Jiao Tong University. He received his B.S. and M.E. from Shanghai Jiao Tong University and Ph.D. degree from the University of Washington, all in electrical engineering.
Xiankai Lu, Ph.D., is a Research Professor at Shandong University. Prior to this role, he was a research associate with Inception Institute of Artificial Intelligence at Abu Dhabi, UAE. Dr. Lu received a B. E. from the Department of Automation at Shan Dong University.
Yunchao Wei, Ph.D, is a Professor in the Center of Digital Media Information Processing at Beijing Jiaotong University. He received his Ph.D. from Beijing Jiaotong University. His current research interests include visual recognition with imperfect data, image/video segmentation and object detection, and multi-modal perception.
Bibliographic Information
Book Title: Video Object Segmentation
Book Subtitle: Tasks, Datasets, and Methods
Authors: Ning Xu, Weiyao Lin, Xiankai Lu, Yunchao Wei
Series Title: Synthesis Lectures on Computer Vision
DOI: https://doi.org/10.1007/978-3-031-44656-6
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-44655-9Published: 16 December 2023
Softcover ISBN: 978-3-031-44658-0Due: 29 December 2024
eBook ISBN: 978-3-031-44656-6Published: 15 December 2023
Series ISSN: 2153-1056
Series E-ISSN: 2153-1064
Edition Number: 1
Number of Pages: VIII, 187
Number of Illustrations: 2 b/w illustrations, 62 illustrations in colour
Topics: Image Processing and Computer Vision, Computer Imaging, Vision, Pattern Recognition and Graphics, Signal, Image and Speech Processing, Computer Science, general, Pattern Recognition