| 书目名称 | Online Visual Tracking | | 编辑 | Huchuan Lu,Dong Wang | | 视频video | | | 概述 | Comprehensively presents the state of the art in online visual tracking.Covers both theory and practice aspects of the topic, addressing seminal research ideas and also approaching the technology from | | 图书封面 |  | | 描述 | .This book presents the state of the art in online visual tracking, including the motivations, practical algorithms, and experimental evaluations. Visual tracking remains a highly active area of research in Computer Vision and the performance under complex scenarios has substantially improved, driven by the high demand in connection with real-world applications and the recent advances in machine learning. A large variety of new algorithms have been proposed in the literature over the last two decades, with mixed success..Chapters 1 to 6 introduce readers to tracking methods based on online learning algorithms, including sparse representation, dictionary learning, hashing codes, local model, and model fusion. In Chapter 7, visual tracking is formulated as a foreground/background segmentation problem, and tracking methods based on superpixels and end-to-end deep networks are presented. In turn, Chapters 8 and 9 introduce the cutting-edge tracking methods based on correlation filter and deep learning. Chapter 10 summarizes the book and points out potential future research directions for visual tracking. .The book is self-contained and suited for all researchers, professionals and post | | 出版日期 | Book 2019 | | 关键词 | Visual Tracking; Correlation Filter; Sparse Representation; Deep Learning; Dictionary Learning; Hashing; M | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-0469-9 | | isbn_ebook | 978-981-13-0469-9 | | copyright | Springer Nature Singapore Pte Ltd. 2019 |
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发表于 2025-3-21 18:45:26
