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.
| Autorius: | Weiwei Xing, Weibin Liu, Jun Wang, Bowen Song, Lihui Wang, Yuxiang Yang, Shunli Zhang, |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 208 |
| ISBN-10: | 981166241X |
| ISBN-13: | 9789811662416 |
| Formatas: | 241 x 160 x 17 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Visual Object Tracking from Correlation Filter to Deep Learning“
