000890775 000__ 03325cam\a2200433Ii\4500
000890775 001__ 890775
000890775 005__ 20230306150122.0
000890775 006__ m\\\\\o\\d\\\\\\\\
000890775 007__ cr\cn\nnnunnun
000890775 008__ 190603s2019\\\\si\\\\\\ob\\\\000\0\eng\d
000890775 019__ $$a1105194914
000890775 020__ $$a9789811304699$$q(electronic book)
000890775 020__ $$a9811304696$$q(electronic book)
000890775 020__ $$z9789811304682
000890775 0247_ $$a10.1007/978-981-13-0$$2doi
000890775 035__ $$aSP(OCoLC)on1103320251
000890775 035__ $$aSP(OCoLC)1103320251$$z(OCoLC)1105194914
000890775 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dEBLCP$$dLQU$$dGW5XE$$dUKMGB
000890775 049__ $$aISEA
000890775 050_4 $$aTA1634
000890775 08204 $$a006.37$$223
000890775 1001_ $$aLu, Huchuan,$$eauthor.
000890775 24510 $$aOnline visual tracking /$$cHuchuan Lu and Dong Wang.
000890775 264_1 $$aSingapore :$$bSpringer,$$c[2019]
000890775 300__ $$a1 online resource.
000890775 336__ $$atext$$btxt$$2rdacontent
000890775 337__ $$acomputer$$bc$$2rdamedia
000890775 338__ $$aonline resource$$bcr$$2rdacarrier
000890775 504__ $$aIncludes bibliographical references.
000890775 5050_ $$a1. 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.
000890775 506__ $$aAccess limited to authorized users.
000890775 520__ $$aThis 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.
000890775 588__ $$aOnline resource; title from PDF title page (viewed June 4, 2019).
000890775 650_0 $$aComputer vision.
000890775 7001_ $$aWang, Dong,$$eauthor.
000890775 852__ $$bebk
000890775 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-13-0469-9$$zOnline Access$$91397441.1
000890775 909CO $$ooai:library.usi.edu:890775$$pGLOBAL_SET
000890775 980__ $$aEBOOK
000890775 980__ $$aBIB
000890775 982__ $$aEbook
000890775 983__ $$aOnline
000890775 994__ $$a92$$bISE