國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Online visual tracking

Lu, Huchuan.

Online visual tracking

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Online visual trackingby Huchuan Lu, Dong Wang.
作者:	Lu, Huchuan.
其他作者:	Wang, Dong.
出版者:	Singapore :Springer Singapore :2019.
面頁冊數:	x, 128 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Computer vision.
電子資源:	https://doi.org/10.1007/978-981-13-0469-9
ISBN:	9789811304699$q(electronic bk.)

Online visual tracking
Lu, Huchuan.

Online visual tracking[electronic resource] /by Huchuan Lu, Dong Wang. - Singapore :Springer Singapore :2019. - x, 128 p. :ill., digital ;24 cm.

1. Introduction to visual tracking -- 2. Visual Tracking based on Sparse Representation -- 3. Visual Tracking based on Local Model -- 4. Visual Tracking based on Model Fusion -- 5. Tracking by Segmentation -- 6. Correlation Tracking -- 7. Visual Tracking based on Deep Learning -- 8. Conclusions and Future Work.

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 postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website.

ISBN: 9789811304699$q(electronic bk.)

Standard No.: 10.1007/978-981-13-0469-9doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634 / .L8 2019

Dewey Class. No.: 006.37

Online visual tracking
LDR:02870nmm a2200337 a 4500 001 558700
003 DE-He213
005 20190530091529.0
006 m d
007 cr nn 008maaau
008 191219s2019 si s 0 eng d
020 $a 9789811304699$q(electronic bk.)
020 $a 9789811304682$q(paper)
024 7 $a 10.1007/978-981-13-0469-9 $2 doi
035 $a 978-981-13-0469-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634 $b .L8 2019
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .L926 2019
100 1 $a Lu, Huchuan. $3 841418
245 1 0 $a Online visual tracking $h [electronic resource] / $c by Huchuan Lu, Dong Wang.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a x, 128 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction to visual tracking -- 2. Visual Tracking based on Sparse Representation -- 3. Visual Tracking based on Local Model -- 4. Visual Tracking based on Model Fusion -- 5. Tracking by Segmentation -- 6. Correlation Tracking -- 7. Visual Tracking based on Deep Learning -- 8. Conclusions and Future Work.
520 $a 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 postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website.
650 0 $a Computer vision. $3 200113
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Wang, Dong. $3 841419
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-13-0469-9
950 $a Computer Science (Springer-11645)