Video Object Tracking : Tasks, Datasets, and Methods

Lu, Xiankai

ISBN 10: 3031446593 / ISBN 13: 9783031446597

Published by Springer, 2023

About this Item

Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book provides a thorough overview of recent progress in video object tracking, allowing researchers and industrial practitioners to gain a better understanding of the most important problems and developed technologies in the area. Video tracking is a key research area in computer vision and aims to track unique objects in a given video, which are useful for various applications such as video conference, video editing, surveillance, and autonomous driving. This book begins with an introduction to the task of video object tracking, including the most common problem settings. Given the revolution of deep learning in computer vision problems, numerous new tasks, datasets, and methods have been recently proposed in the domain of video tracking. The book includes these recent results as well as benchmarks in large-scale human-centric video analysis in complex events.

Seller Inventory # 42699878/1

Bibliographic Details

• Title: Video Object Tracking : Tasks, Datasets, and Methods
• Publisher: Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 3031446593
• ISBN 13: 9783031446597
• Binding: Hardcover
• Condition: Hervorragend
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book provides a thorough overview of recent progress in video object tracking, allowing researchers and industrial practitioners to gain a better understanding of the most important problems and developed technologies in the area. Video tracking is a key research area in computer vision and aims to track unique objects in a given video, which are useful for various applications such as video conference, video editing, surveillance, and autonomous driving. This book begins with an introduction to the task of video object tracking, including the most common problem settings. Given the revolution of deep learning in computer vision problems, numerous new tasks, datasets, and methods have been recently proposed in the domain of video tracking. The book includes these recent results as well as benchmarks in large-scale human-centric video analysis in complex events. 

About the Author

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.