| 書目名稱 | Visual Object Tracking from Correlation Filter to Deep Learning | | 編輯 | Weiwei Xing,Weibin Liu,Bowen Song | | 視頻video | http://file.papertrans.cn/984/983759/983759.mp4 | | 概述 | 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 wi | | 圖書封面 |  | | 描述 | 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. | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | Visual Object Tracking; Correlation Filter; Deep Learning; Reinforcement Learning; Siamese Network; Gener | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-16-6242-3 | | isbn_softcover | 978-981-16-6244-7 | | isbn_ebook | 978-981-16-6242-3 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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