Artículos relacionados a Visual Object Tracking from Correlation Filter to Deep...
Sinopsis
The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Weiwei Xing received the B.S. degree in computer science and technology and the Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, Beijing, China, in 2001 and 2006, respectively. She was also a visiting researcher at Department of Computer Science in University of Pennsylvania, PA, USA from Feb.2011 to Feb. 2012. She is currently a Professor at School of Software Engineering, Beijing Jiaotong University. Her research interests include video information processing, computer vison, machine learning, big data analysis and software engineering.
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.
De la contraportada
The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialSpringer
- Año de publicación2022
- ISBN 10 9811662444
- ISBN 13 9789811662447
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas208
- Contacto del fabricanteno disponible
Comprar nuevo
Ver este artículo
EUR 127,40
EUR 19,49 gastos de envío desde Alemania a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Visual Object Tracking from Correlation Filter to Deep...
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Publicado por
Springer Nature Singapore, 2022
ISBN 10: 9811662444
ISBN 13: 9789811662447
Antiguo o usado
Tapa blanda
Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 208 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 40766655/1
Cantidad disponible: 2 disponibles
Imagen del vendedor
Visual Object Tracking from Correlation Filter to Deep Learning
Publicado por
Springer, Berlin|Springer Nature Singapore|Springer Singapur|Springer, 2022
ISBN 10: 9811662444
ISBN 13: 9789811662447
Nuevo
Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three k. Nº de ref. del artículo: 743990355
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9789811662447_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Visual Object Tracking from Correlation Filter to Deep Learning
Publicado por
Springer Nature Singapore Nov 2022, 2022
ISBN 10: 9811662444
ISBN 13: 9789811662447
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields. 208 pp. Englisch. Nº de ref. del artículo: 9789811662447
Cantidad disponible: 2 disponibles
Imagen del vendedor
Visual Object Tracking from Correlation Filter to Deep Learning
Publicado por
Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811662444
ISBN 13: 9789811662447
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields. Nº de ref. del artículo: 9789811662447
Cantidad disponible: 1 disponibles
Imagen del vendedor
Visual Object Tracking from Correlation Filter to Deep Learning
Publicado por
Springer Nature Singapore, Springer Nature Singapore Nov 2022, 2022
ISBN 10: 9811662444
ISBN 13: 9789811662447
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch. Nº de ref. del artículo: 9789811662447
Cantidad disponible: 1 disponibles
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9789811662447
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26396347228
Cantidad disponible: 4 disponibles
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand. Nº de ref. del artículo: 401111171
Cantidad disponible: 4 disponibles
Imagen de archivo
Visual Object Tracking from Correlation Filter to Deep Learning
Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. 207 pages. 9.25x6.10x0.59 inches. In Stock. Nº de ref. del artículo: x-9811662444
Cantidad disponible: 2 disponibles